Bibliographic record
Abstract
• LCA is a static, training database for learning how to use the CA database • Bibliographic information and available abstracts • Cited references for journals, conference proceedings, and basic patents from the US, EPO, WIPO, and German patent offices added to CAS databases since 1997 • Patent examiner citations from British and French patents (2003-present), Canadian patents (2005-present) as well as nearly 300,000 patent records from 1982-2008 • Citing references • Patent classifications: IPC, CPC, ECLA, ICO, NCL and FTERM File Size 64,658 records (9/2014) Coverage References from eight weekly issues of Chemical Abstracts (CA), two each from 1969, 1974, 1979 and 1984 (representing the 8th, 9th, 10th and 11th Collective Index periods), as well as references from LCASREACT (1987-1988). Additional records have been added to illustrate new features and functionality. Updates None
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 | 0.005 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.024 | 0.048 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.826 | 0.691 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".