[30] T. W. Yan, and H. Garcia-Molina.
Bibliographic record
Abstract
Estimating the Usefulness of Search Engines. 15th International Conference on Data Engineering (ICDE'99), Sydney, Australia, March 1999 (to appear). [22] Networked Computer Science Technical Reports Library, http://lite.ncstrl.org:3803/. [23] A. Singhal, C. Buckley. and M. Mitra. Pivoted Document Length Normalization. ACM SIGIR Conference, Zurich, 1996. [24] G. Salton and M. McGill. Introduction to Modern Information Retrieval. New York: McCrawHill, 1983. [25] G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison Wesley, 1989. [26] E. Selberg, and O. Etzioni. Multi-Service Search and Comparison Using the MetaCrawler. 4th International World Wide Web Conference, December 1995. [27] E. Selberg, and O. Etzioni. The MetaCrawler Architecture for Resource Aggregation on the Web. IEEE Expert, 1997. [28] G. Towell, E. Voorhees, N. Gupta, and B.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".