Bibliographic record
Abstract
It is well known that the sequential approach is one of the main tools of dealing with product, power, and convolution of distribution (cf. Chen (1981), Colombeau (1985), Jones (1973), and Rosinger (1987)). Antosik, Mikusiński, and Sikorski in 1972 introduced a definition for a product of distributions using a delta sequence. However, δ2 as a product of δ with itself was shown not to exist (see Antosik, Mikusiński, and Sikorski (1973)). Later, Koh and Li (1992) chose a fixed δ‐sequence without compact support and used the concept of neutrix limit of van der Corput to define δk and for some values of k. To extend such an approach from one‐dimensional space to m‐dimensional, Li and Fisher (1990) constructed a delta sequence, which is infinitely differentiable with respect to x1, x2, …, xm and r, to deduce a non‐commutative neutrix product of r−k and Δδ. Li (1999) also provided a modified δ‐sequence and defined a new distribution (dk/drk)δ(x), which is used to compute the more general product of r−k and Δlδ, where l ≥ 1, by applying the normalization procedure due to Gel′fand and Shilov (1964). We begin this paper by distributionally normalizing Δr−k with the help of distribution . Then we utilize several nice properties of the δ‐sequence by Li and Fisher (1990) and an identity of δ distribution to derive the product Δr−k · δ based on the results obtained by Li (2000), and Li and Fisher (1990).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".