Bibliographic record
Abstract
One of the great defects of English books printed in the last century is the want of an index.-Lafcadio Hearn a.k.aKoizumi Yakumo 1+1 redundancy, 207 1xEV-DO, 207-208 2-way tests, See pairwise testing 3-way tests, 235 4-way tests, 235 80/20 ratio, 507 80/20 rule, See Pareto analysis AAA server, 208 ability to perform, 553 Abnormal (A), 114-115 abstract syntax notation one (ASN.1),201 ACC, See acceptance criteria change acceptance beta, 435 criteria, 451, 461 criteria change (ACC), 464 test engineer, 464 test execution, 463-464 test in XP, 466-467 test plan, 461-463 test report, 464-466 testing, 17, 450 access audit, 529 control, 529 terminal (AT), 208 accident, See mishap accomplish, 508 accounting management, 199 accuracy, 453, 529, 531 acoustic test, 176 actions , 365 active mode, 207 Software Testing and Quality Assurance: Theory and Practice, Edited by Kshirasagar Naik and Priyadarshi Tripathy
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.734 | 0.648 |
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".