Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indexing Web pages based on content is a crucial step in a modern search engine. A variety of methods and approaches exist to support web page rankings. In this paper, we describe a new approach for obtaining measures for Web page ranking. Unlike other recent approaches, it exploits the meta-terms extracted from the titles and urls for indexing the contents of web documents. We use the term impact to correlate each meta-term with document's content, rather than term frequency and other similar techniques. Our approach also uses the structural knowledge available in Wikipedia for making better expansion and formulation for the queries. Evaluation with automatic metrics provided by TREC reveals that our approach is effective for building the index and for retrieval. We present retrieval results from the ClueWeb collection, for a set of test queries, for two tasks: for an adhoc retrieval task and for a diversity task (which aims at retrieving relevant pages that cover different aspects of the queries).
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.
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 | 0.001 | 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 it