A categorization theorem on suffix arrays with applications to space efficient text indexes
Bibliographic record
Abstract
In this paper, we design succinct index structures for a text string T of n binary symbols to support efficient searching of a pattern P of length m. Motivated by the fact that the standard representation of suffix arrays uses n lg n bits which is more than the theoretical minimum, we present a theorem that characterizes a permutation as the suffix array of a binary string. Based on the theorem, we design a succinct representation of suffix arrays of binary strings that uses n + o(n) bits, which is the theoretical minimum plus a lower order term, and answers existential and cardinality queries in O(m) time without storing the raw text. With 2n+o(n) bits, we can list pattern occurrences in O(m + occ lg n) time in the general case, and for long patterns, when m = Ω(lg1+∈n), we answer such listing queries in O(m + occ) time. We also present another implementation that uses O(n) bits and supports pattern searching in O(m + occ lgλn) time for any fixed λ such that 0
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".