Bibliographic record
Abstract
A binary trie is a sequential data structure that maintains a dynamic set from the universe$\{0,\ \ldots,\ u-1\}$, supporting Search with$O(1)$worst-case step complexity, and Insert, Delete, and Predecessor with$O(\log u)$worst-case step complexity. We give a wait-free implementation of a relaxed binary trie, using read, write, CAS, and AND operations. It supports all oper-ations with the same worst-case step complexity as the sequential binary trie. However, predecessor operations may not return a key when there are concurrent update operations. We use this as a component of a lock-free, linearizable implementation of a binary trie. It supports Search with$O(1)$worst-case step complexity and Insert, Deleteand Predecessorwith$O(c^{2}+\log\ u)$amortized step complexity, where$c$is a measure of the contention. A lock-free binary trie is challenging to implement as compared to many other lock-free data structures because Insertand Deleteoperations perform a non-constant number of modifications to the binary trie in the worst-case to ensure the correctness of Predecessoroperations.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".