Differential calculus on topological spaces with weak Markov structure I
Bibliographic record
Abstract
The concept of a weak Markov set takes its origin from Whitney problems for differentiable functions on \mathbb R^n . In the present paper we study a version of the first Whitney problem of characterizing families of continuous functions satisfying certain differential relations on weak Markov sets. To this end we develop differential calculus on weak Markov sets similar to that on open subsets of \mathbb R^n . Then we show that some classical results for smooth functions and differential forms (such as Poincaré lemma, de Rham and Hartogs theorems, Künneth formulas, etc.) are valid also on certain weak Markov sets and more generally certain topological spaces with weak Markov structures. The class of such spaces includes, in particular, C^{\infty} manifolds with boundaries and some Lipschitz and fractal topological manifolds. Thus the paper offers yet another approach to analysis on fractals, a developing area of modern mathematics that focuses on geometric and dynamical aspects of fractals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".