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
There are three main problems when designing an information retrieval (IR) system, namely, uncertainty in the representation of documents and queries, computational complexity, and the diversity of users. An IR system may be designed to be adaptive by allowing the modification of document and query representation. As well, different retrieval methods can be used for different users. The combina-tion of multi-representation of documents and multi-strategy retrieval may provide a Solution for the diversity of users. A widely used Solution for reducing computational costs is cluster-based retrieval. However, the use of document clustering only reduces the dimensionality of documents. The same number of terms is used for the representation of the Clusters. One may reduce the dimensionality of terms by constructing a term hierarchy in parallel to the construction of a document hierarchy. The proposed framework of granular IR enables us to incorporate multi-representation of documents and multi-strategy retrieval. Hence, granular IR may provide a method for developing knowledge based intelligent IR Systems.
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) | 0.001 | 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 | 0.001 |
| 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