Bibliographic record
Abstract
Abstract LC tries were introduced by Andersson and Nilsson in 1993. They are compacted versions of tries or patricia tries in which, from the top down, maximal height complete subtrees are level compressed. Andersson and Nilsson (1993) showed that for i.i.d. uniformly distributed input strings, the expected depth of the LC patricia trie is Θ(log* n ). In this article, we refine and extend this result. We analyze both kinds of LC tries for the uniform model, and study the depth of a typical node and the height H n . For example, we show that H n is in probability asymptotic to log 2 n and \documentclass{article}\pagestyle{empty}\begin{document}$\sqrt{2 \log_2 n}$\end{document} for the LC trie and the LC patricia trie, respectively, and that for both the tries, the depth of a typical node is asymptotic to log*( n ) in probability and in expectation. © 2001 John Wiley & Sons, Inc. Random Struct. Alg., 19: 359–375, 2001
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".