Upper bounds on the general covering number <i>C</i><sub>λ</sub>(<i>v</i>, <i>k</i>, <i>t</i>, <i>m</i>)
Bibliographic record
Abstract
Abstract A collection $\cal C$ of k ‐subsets (called blocks ) of a v ‐set X ( v ) = {1, 2,…, v } (with elements called points ) is called a t ‐( v , k , m , λ) covering if for every m ‐subset M of X ( v ) there is a subcollection $\cal K$ of $\cal C$ with $|\cal K|\geq \lambda$ such that every block K ∈ $\cal K$ has at least t points in common with M . It is required that v ≥ k ≥ t and v ≥ m ≥ t . The minimum number of blocks in a t ‐( v , k , m , λ) covering is denoted by C λ ( v , k , t , m ). We present some constructions producing the best known upper bounds on C λ ( v , k , t , m ) for k = 6, a parameter of interest to lottery players. © 2004 Wiley Periodicals, Inc.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".