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Record W4413399490 · doi:10.1007/s10959-025-01439-4

On Monotonic Functionals Over Partially-Ordered Path Spaces

2025· article· en· W4413399490 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Theoretical Probability · 2025
Typearticle
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMathematicsMonotonic functionPath (computing)Pure mathematicsCombinatoricsMathematical analysisComputer science

Abstract

fetched live from OpenAlex

Abstract We study non-decreasing (non-increasing) monotonic functionals over unions of Skorokhod spaces of càdlàg paths which we show to have an equivalence to non-negative (non-positive) path-dependent spatial Dupire derivatives. These functionals provide an upper-bound for their Lie-bracket of non-commutative spatial and temporal Dupire operators. We provide a stochastic functional generalisation for the Lebesgue integral of derivatives of non-decreasing (non-increasing) functions. We also present a functional generalisation of Markov’s inequality. One can further associate monotonic functionals of order-preserving random paths to their stochastic differential equations. We encapsulate what we call buffered monotonic functionals on paths that never draw closer than a minimum distance over their lifetime. As an application, we generate path-dependent stochastic triangles that randomly change their location, shape and area, while embedding a minimum structure that ensures convexity of the geometry at every point in time—a construct for modelling temporal population cluster dynamics with memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.320
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it