How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Irreversible Boundaries — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Analyticity of Holomorphic Functions; Liouville and Morera
- Approximation and Compactness in C(K)
- Arc Length and Rectifiable Curves
- Areas of Elementary Plane Figures
- Banach Alaoglu Goldstine and Krein Milman
- Banach Algebras Spectrum and Holomorphic Functional Calculus
- Banach Valued Integration and the Radon Nikodym Property
- Bessel-Potential Completions and Real-Order Sobolev Spaces
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Bounded Variation and the Riemann–Stieltjes Integral
- Cm Math Geometry
- Compact Operators and Riesz Schauder Theory
- Compact Self Adjoint Hilbert Schmidt and Trace Class Operators
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Differentiability and the Cauchy–Riemann Equations
- Complex Lp Spaces and Test-Function Conventions
- Complex Power Series and Analytic Functions
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Connectedness
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Continuous Functional Calculus for Self Adjoint and Normal Operators
- Contour Integration
- Convergence: Nets and Filters
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Cyclic Groups and Direct Products
- Darboux, L'Hôpital, and Taylor's Theorem
- Density Separability and Convolution in Lᵖ
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Distributions Test Functions and Differentiation
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces Adjoint Operators and Annihilators
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Entropy Foundation
- Euclidean Surface Measure, Divergence, and Green Identities
- Exterior Powers, Orientation and Hodge Duality
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Finite Probability and the Probabilistic Method
- Finite Probability Spaces and Random Variables
- Finite Statistical Bridge
- Foundations of the Real Numbers for Analysis
- Fourier Multipliers and Sobolev Characterisations
- Fourier Transform Convolution and Approximate Identities
- Fredholm Elliptic Problems and the Elliptic Spectrum
- Free Modules, Exact Sequences, Projective and Injective Modules
- Fubini and Change of Variables
- Function Space Topologies and the Exponential Law
- Fundamental Trigonometric Identities
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Gelfand Theory and Commutative C Star Algebras
- Goursat's Theorem and Cauchy's Theorem in a Convex Domain
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Harmonic Functions and Mean Values in Rn
- Hausdorff via the Diagonal
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Improper and Parameter-Dependent Multiple Integrals
- Improper Integrals
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Irreversible Boundaries
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Line Integrals and the Gradient Theorem
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Modes of Convergence Egorov and Lusin
- Modules, Submodules, Quotient Modules and the Isomorphism Theorems
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Norming and Separation under Hahn–Banach
- Order, Zorn's Lemma, and the Axiom of Choice
- Orthonormal Bases, Parseval and Fourier Series
- Outer Measure and the Caratheodory Extension Theorem
- Partitions of Unity and Paracompactness
- pi: the Equivalent Characterizations
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Probability Spaces Random Variables and Expectation
- Product Measures and the Fubini Tonelli Theorems
- Properties of the Integral and the Working FTC
- Qm Autonomous Dynamics
- Qm Framework And Quantities
- Qm Identical Particles And Exclusion
- Qm Many Body And Equilibrium Models
- Qm Massive Particle Kinematics
- Qm Math Composites
- Qm Math Coulomb
- Qm Math Fock And Equilibrium
- Qm Math Magnetic And Detection
- Qm Math Orbital And Spin
- Qm Observables And Born Probabilities
- Qm State Geometry
- Radon Measures and the Riesz Markov Kakutani Theorem
- Regular Surfaces and Surface Integrals
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Schwartz Space and the Plancherel Theorem
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Smooth Approximation and Sobolev Extension
- Smooth Partitions of Unity and Exhaustions
- Spectral Measures and Borel Functional Calculus
- Splitting Fields
- Stone–Weierstrass in General
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tempered Distributions and the Fourier Transform
- Tensor Products of Modules
- The Analytic Hahn Banach Theorem
- The Baire Principles of Functional Analysis
- The Branching Rule and the Young Graph
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Divergence Theorem and Classical Stokes
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Fundamental Theorems of Calculus
- The Galois Correspondence
- The Group Algebra and Representations of Finite Groups
- The Inverse and Implicit Function Theorems
- The Inverse Function Theorem Completed
- The Lebesgue and Riemann Integrals Compared
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Maximal Function and Lebesgue Differentiation
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Thermodynamic Framework
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Unbounded Self Adjoint Operators and Stones Theorem
- Uniform Spaces: the Three Definitions
- Unitary Representations, Positive Type and GNS
- Urysohn's Lemma and the Tietze Extension Theorem
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Volumes of Elementary Solids and Solids of Revolution
- Weak Derivatives and Sobolev Spaces
2 · Summary
Conditional finite models distinguish entropy definitions and ensemble averages from single outcomes. The continuum examples keep local equilibrium and transport relations within explicitly chosen model inputs. The final item is an empty evidence template; it reports no observations.
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
None yet.
5 · Examples, experiments and counterexamples
A macrocell count and a nonuniform microscopic distribution
A stipulated two-microstate finite model with a nonuniform probability vector; it is not an equilibrium preparation, measurement or empirical claim.
Example
Let with , and declare the finite numerical energy map by , so the assigned physical energies are . This supplies the finite microstate pair required in Macroscopic and microscopic entropy quantities; no mechanical internal-energy or equilibrium-macrostate identification is assumed. Choose and the constant coarse-graining map , so both states belong to the macrocell , and assign . The probability vector is deliberately nonuniform. The example shows that the macrocell-count definition and the Gibbs–Shannon definition can take different values on separately specified inputs.
Facts & Assumptions
Given: The finite model below and the entropy definitions.
Macroscopic and microscopic entropy quantities: , while , with the zero-probability summand defined as zero.
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: is strictly increasing on and .
The two states are distinct, is identically zero, and is constant. Thus has , and is a probability vector.
Verification
Given: [F1]–[F2], [L1], and [A1].
Since , the macrocell entropy is .
The only nonzero probability term is ; the term is zero by definition. Hence .
Since , [L1] gives . With from [F2], step 1.1 is strictly positive while step 1.2 is zero, so these two defined quantities differ in this model. This example asserts no general inequality or thermodynamic identification.
Positive ensemble mean with a negative realization
A two-outcome probability model for an illustrative entropy-valued random variable; it is not a specified physical fluctuation law or reported observation.
Example
Let with weights and . Both weights are nonnegative and sum to , so is a finite probability space as defined in Finite probability spaces, outcome weights, events, and event probabilities. For every event , write for its event probability. Let be given by and , where and the values are numerical values in the unit . By Real random variables on finite probability spaces and their finite distributions, is a finite-valued real random variable on this finite probability space, so its expectation is a finite sum. The label is only an illustrative entropy-valued variable; this example adopts no physical path law.
This arithmetic example illustrates the distinction in Hypotheses for microscopic fluctuation claims.
Facts & Assumptions
Given: The two-outcome model and assignments stated above.
Expectation is the sum of each attained value times its probability: for a real random variable on a finite probability space, .
Finite probability spaces, outcome weights, events, and event probabilities: nonnegative weights summing to define a finite probability space, and each event probability is the sum of its outcome weights.
Real random variables on finite probability spaces and their finite distributions: every function from a finite probability space to is a finite-valued real random variable.
The assignments are , , , , and .
Verification
Given: [F1]–[F3] and [A1].
The two values of are distinct because . By [F2]–[F3], is a real random variable on the stated finite probability space, so [F1] gives . Its unit is .
The event is exactly , so by [F2] its probability is .
Steps 1.1 and 1.2 exhibit a positive mean together with a negative realization of nonzero probability. Therefore positivity of an ensemble mean alone does not imply a nonnegative value in every trial. The example is not a fluctuation theorem or a claim about measured entropy production.
A nonconstant local-equilibrium temperature field
A stipulated static local-equilibrium field on an open cube; no global equilibrium, transport law or observation is asserted.
Example
Use the one-coordinate convention of The Euclidean inner product on explicitly: sends to the function on with value , and the inverse takes the coordinate at index zero. In every scalar display below, a one-coordinate parameter or field is written through this bijection. Thus means , the scalar formula for denotes its image, means , is the identity on , and ; temperature and inequalities use the scalar coordinate . This is a notation convention, not a set-theoretic identity between a real and a function.
Take in metre coordinates and in second coordinates, with and . Choose and dimensionless . Instantiate and the local-state parameter set , and define its one-coordinate field by
For the homogeneous comparison cell, supply with states , physical energy , and numerical entropy , where is the natural logarithm of The natural logarithm as the inverse of the exponential function and physical entropy is . The coordinate is dimensionless. Set , , , and for every , so the fixed-energy admissible set is . Its entropy-maximizing set is . Define and supply ; assign this comparison state temperature . Stipulate the local-equilibrium approximation with this map, so the local constitutive state at is . The field is static and nonconstant; this stipulation is a model assumption, not an assertion that a physical sample realizes the profile.
Facts & Assumptions
Given: The local-field convention of Scope of a local-equilibrium continuum branch and the parameters just specified.
Scope of a local-equilibrium continuum branch: the local-state field has the displayed domain and its components have continuous first spatial and time partial derivatives; a temperature component is read in kelvins.
Scope of a local-equilibrium continuum branch: an entropy-based local-equilibrium model supplies cell states, entropy, retained constraints and conserved-value maps; is the entropy-maximizing subset of , and the supplied map must satisfy . Its pointwise comparison state is ; attainment is verified for the supplied model rather than inferred from a label.
Scope of a local-equilibrium continuum branch: nonconstant temperature fields are permitted in this continuum model; the pointwise comparison-cell criterion alone neither establishes nor excludes equilibrium of the whole system.
The natural logarithm as the inverse of the exponential function defines , so the supplied cell entropy is finite at every cell state.
has metre coordinates in , , dimensionless , , , and with ; the cell data are exactly those in the Example and ; the model stipulates the resulting pointwise local-equilibrium assignment.
Verification
Given: [F1]–[F4] and [A1].
Since , , so and . The affine formulas are on and meet the field regularity in [F1].
Choose interior points with and , keeping the other coordinates fixed. Their temperature difference is , so the field is not constant.
For each , [A1] gives and , with equality exactly at . Thus and , meeting the constrained-maximum criterion in [F2]. The logarithm is finite since . By [F1] and step 1.1, the one-coordinate field has the declared range and regularity, so its supplied comparison state is . Steps 1.1–1.2 show that its temperature component is positive and spatially nonconstant; [F3] says this need not describe global equilibrium. No dynamics or experimental outcome is inferred.
Different Fourier coefficients give different model fluxes
Comparison of two stipulated isotropic Fourier models on the same temperature profile; no material measurement or universal constitutive law is asserted.
Example
On in metre coordinates and in second coordinates, with and , prescribe , where and . In the chosen Cartesian coordinates, define . Compare two isotropic model choices , , each using . The coefficients have units .
Facts & Assumptions
Given: The Fourier-model definition and the stipulated fields and coefficients.
Scope of transport constitutive laws: for a temperature field with continuous first spatial partial derivatives, in the physical one-based coordinate display (function indices ), and a Fourier model may postulate , where has entries in ; its isotropic form is with .
, , ; in the chosen Cartesian coordinates; in with ; both models use the same profile ; and model adopts .
Verification
Given: [F1] and [A1].
The common temperature gradient is . The two assigned model fluxes are therefore and .
Their difference is . The same field profile therefore has different predicted fluxes under these two constitutive choices.
The calculation uses the Fourier relation as a stated model input and does not determine either from equilibrium entropy data. It is a model comparison, not a measured result.
Empty source-backed evidence record template
Documentation template only; it contains no experiment, observation, apparatus reading or uncertainty value.
Example
The following empty record shows fields to fill from an actual source report. Bracketed text is an instruction, not a result or measurement.
The fields implement Evidence records for irreversible and transport claims.
- Source report and exact locator: [required]
- Material, sample geometry, apparatus and boundary conditions: [required]
- Operational definitions and calibration: [required]
- Preparation, controls, sampling procedure and sample size: [copy only what the source reports]
- Observations with units and stated uncertainty: [copy only source-supported values]
- Systematic limitations and missing information: [state explicitly]
- Model prediction and assumptions: [keep separate from observations]
- Statistical method and conditional conclusion: [state method, sampling assumptions and limits]
No claim of a performed experiment is made by this template.