Legendre pairs of lengths<i>ℓ</i>≡ 0 (mod 5)
Bibliographic record
Abstract
Abstract By assuming a type of balance for length <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mi>ℓ</m:mi><m:mo>=</m:mo><m:mn>87</m:mn></m:math> \ell =87 and nontrivial subgroups of multiplier groups of Legendre pairs (LPs) for length <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mi>ℓ</m:mi><m:mo>=</m:mo><m:mn>85</m:mn></m:math> \ell =85 , we find LPs of these lengths. We then study the power spectral density (PSD) values of <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mi>m</m:mi></m:math> m compressions of LPs of length <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mn>5</m:mn><m:mi>m</m:mi></m:math> 5m . We also formulate a conjecture for LPs of lengths <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mi>ℓ</m:mi><m:mo>≡</m:mo><m:mn>0</m:mn></m:math> \ell \equiv 0 (mod 5) and demonstrate how it can be used to decrease the search space and storage requirements for finding such LPs. The newly found LPs decrease the number of integers in the range <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mo>≤</m:mo><m:mn>200</m:mn></m:math> \le 200 for which the existence question of LPs remains unsolved from 12 to 10.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".