Bibliographic record
Abstract
v 13.4.2Lower Bound for Polygons with Two or Three Narrow Vertices 67 14 conclusion and future work 71 III art gallery localization 73 15 introduction 74 16 preliminaries 76 17 tóth's partition 80 17.1 Case Study 81 17.2 Point-Kernel Problem 82 17.3 n = 3k + 2 and P ′ has no good diagonal dissection 85 17.3.1 Adaptation of Tóth's Lemma 3 to our problem 85 17.3.2Proof of Tóth's Lemma 2 and its adaptation to our problem 86 17.4 Partition algorithm 92 17.5 Counterexample to Tóth's conjecture 93 18 localization algorithm 95 19 concluding remarks 96 IV optimal art gallery localization is np-hard 97 20 introduction 98 21 preliminaries 99 22 optimal art gallery localization is np-hard 101 22.1 Literal Pattern 101 22.2 Clause Junction 102 22.3 Variable Pattern 103 22.4 Complete Construction 105 22.5 Construction takes Polynomial Time.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".