An Improved Version of the Runs Algorithm Based on Crochemore's Partitioning Algorithm.
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it