Bibliographic record
Abstract
In this note, I sketch a proof of a conjecture by Mike Zabrocki. The writing is rough and the proof probably not readable without preparation. Possibly, a more readable (and self-contained?) version will be presented in a paper which is currently being written [Grinb14]. 0.1. Acknowledgments Mike Zabrocki kindly shared his conjecture with me during my visit to Univer-sity of York, Toronto in March 2014; I am further grateful to Nantel Bergeron for the invitation and the hospitality. 1. Quasisymmetric functions We refer to [BBSSZ13, Section 2] for the definitions and notations which we will be using. We use N to denote the set {0, 1, 2,...}. Our symmetric and quasisymmetric functions are defined over a commutative ring k. They live in the k-algebra k [[x1, x2, x3,...]]bdd of bounded-degree power series in k [[x1, x2, x3,...]] (that is, of all power series whose monomials have their degrees bounded from above). When we speak of “monomials”, we always
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".