Bibliographic record
Abstract
An important question in information retrieval is how to create a database index which can be searched efficiently for the data one seeks. Today, one or more of the following four techniques have been frequently used: full text searching, B-trees, inversion and the signature file. Full text searching imposes no space overhead, but requires long response time. In contrast, B-trees, inversion and the signature file work quickly, but need a large intermediary representation structure (index), which provides direct links to relevant data. In this paper, we concentrate on the techniques of signature files and discuss different construction approaches of a signature file. The signature technique cannot only be used in document databases, but also in relational and object-oriented databases. In a document database, a set of semistructured (XML) documents is stored and the queries related to keywords are frequently evaluated. To speed up the evaluation of such queries, we can construct signatures for words and superimpose them to establish signatures for document blocks, which can be used to cut off non-relevant documents as early as possible when evaluating a query. Especially, such a method can be extended to handle the socalled containment queries, for which not only the key words, but also the hierarchical structure of a document has to be considered. We can also handle queries issued to a relational or an objectoriented database using the signature technique by establishing signatures for attribute values, tuples, as well as tables and classes.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.022 |
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".