Swe88] J. A. Swets, Measuring the accuracy of diagnostic systems, Science, 240(4857): 1285-
Bibliographic record
Abstract
dition, 1991. [Sho92] L. Shoshana, Architechtng Personalized Delivery of Multimedia Information, CACM, 35(12), 39-50, December 1992. [SKB+98] B. M. Sarwar, J. A. Konstan, A. Borchers, J. L. Herlocker, B. N. Miller, and J. Riedl, Using Filtering Agents to Improve Prediction Quality in the Groupiens Research Collaborative Filtering System. In Proc. ACM Conf Computer Support Cooperativ Work (CSCVO 1998, Seattle, WA., page 345-354 Novemer 1998. [SKR99] J. B. Schafer, J. Konstan, and J. Riedl, Recommender Systems in E-Commerce, ACM Conf. Electronic Commerce (EC-99), Denver, CO, pages 158-166, November 1999. [SMc83] G. Salton and M. J. McGill, Introduction to Modern Information Retrieval, McGraw- Hill, 1983. [SM95] U. Shardanand and P. Maes, Social information filtering: Algorithms for automating "word of mouth." In Proc. 1995 ACM Conf. Human Factors in Computing Systems, New York, NY, pages 210-217, 1995. BmiIOGmPh'Y 101 [HCC98] J. Han, S. Chee, and J. Y. Chiang, Issues for On-Line
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.018 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".