Bibliographic record
Abstract
With the massive and fast-growing amount of information on the Web, maintaining the effectiveness of Information Retrieval (IR) is a real challenge. The system in charge of online search must be able to search through billions of documents stored on millions of devices (Manning Christopher D et al., 2010). Traditional information retrieval systems try to sort out the input queries by mostly emphasizing on lexical similarity and exact term matching between query and documents using frequency-based methods. In other words, the relevancy of a query to a document is viewed based on the closeness of the distribution of words in a candidate document to the query. Since the lexical content of the optimal response is not usually known to the user, the user formulates a query with vocabulary that may have minimal overlap with the vocabulary appearing in its optimal document. Low overlap between query and document vocabulary is called term mismatch which emerges in retrieval results as poor recall performance. The term mismatch problem also has been referred to as lexical gap or lexical chasm with query on one side of the gap and documents on the other side. IR systems use different techniques to bridge the lexical chasm and solve the term mismatch problem. Many different query refinement techniques have already been developed. Given the user query, each refinement technique outputs a modified version of user’s query that can be used as an arch over the lexical gap from the query side to the document side.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.029 |
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".