Bibliographic record
Abstract
Let c(x) be a monic integer polynomial with coefficients 0 or 1. Write c(x)=a(x)b(x) where a(x) and b(x) are monic polynomials with non-negative real coefficients (not necessarily integer). The unfair 0–1 polynomial conjecture states that a(x) and b(x) are necessarily integer polynomials with coefficients 0 or 1. Let a(x) be a candidate factor of a (currently unknown) 0–1 polynomial. We will assume that we know if a coefficient is 0, 1 or strictly between 0 and 1, but that we do not know the precise value of non-integer coefficients. Given this candidate a(x), this paper gives an algorithm to find a b(x) and c(x) with a(x)b(x)=c(x) such that b(x) has non-negative real coefficients and c(x) has coefficients 0 or 1, or (often) shows that such c(x) and b(x) do not exist. Using this algorithm, we consider all candidate factors with degree less than or equal to 15. With the exception of 975 candidate factors (out of a possible 7141686 cases), this algorithm shows that there do not exist b(x) with non-negative real coefficients and c(x) with coefficients 0 or 1 such that a(x)b(x)=c(x).
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 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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".