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
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".