Bibliographic record
Abstract
The prehistory of our subject can be backdated to 1202, with the appearance of Leonardo Pisano's Liber Abaci (Fibonacci 1202), containing the famous problem about breeding rabbits that leads to the linear recursion f n +1 = f n + f n −1 for n ≥ 2, f 1 = f 2 = 1, which yields the Fibonacci sequence. Additional background can be attributed to Euler, Gauss, Kummer, and especially Edouard Lucas (Lucas 1876). For the history proper, the earliest milestones are papers by O. Ore (Ore 1934), R.E.A.C. Paley (Paley 1933), and J. Singer (Singer 1938). Ore started the systematic study of linear recursions over finite fields (including GF (2)), Paley inaugurated the search for constructions yielding Hadamard matrices, and Singer discovered the Singer difference sets that are mathematically equivalent to binary maximum length linear shift register sequences (also known as pseudorandom sequences, pseudonoise (PN) sequences, or m -sequences). It appears that by the early 1950s devices that performed the modulo 2 sum of two positions on a binary delay line were being considered as key generators for stream ciphers in cryptographical applications. The question of what the periodicity of the resulting output sequence would be seemed initially mysterious. This question was explored outside the cryptographic community by researchers at a number of locations in the 1953–1956 time period, resulting in company reports by E. N. Gilbert at Bell Laboratories, by N. Zierler at Lincoln Laboratories, by L. R. Welch at the Jet Propulsion Laboratory, by S.W. Golomb at the Glenn L. Martin Company (now part of Lockheed-Martin), and probably by others as well.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.217 | 0.108 |
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".