An Improved Version of the Runs Algorithm Based on Crochemore's Partitioning Algorithm.
Bibliographic record
Abstract
Abstract. Though there are in theory linear-time algorithms for computing runs in strings, recently two of the authors implemented an O(n log n) algorithm to compute runs that was based on the Crochemore’s partitioning repetitions algorithm. The al-gorithm preserved the running complexity of the underlying Crochemore’s algorithm; however, the static memory requirement – already large at 14n integers for a string of length n – was increased significantly to O(n log n) integers. The purpose and advantage of this algorithm was its speed. In this paper we present a more advanced version of the extension of the Crochemore’s algorithm for computing runs. This version in addi-tion to maximal repetitions, computes runs and primitively rooted distinct squares. Its implementation completely does away with the extra memory required for the previous version and through some additional memory saving techniques, the overall memory need was reduced to 13n integers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".