Bibliographic record
Abstract
ANOVA analysis of variance method ANZTPA Australia New Zealand Therapeutic Products Authority BAFS biologically active food supplements (Russia) BNSFD Bureau of Nutritional Sciences Food Directorate (Canada) BRIC Brazil, Russia, India and China CFDA China Food and Drug Administration CFIA Canadian Food Inspection Agency (CFIA) CHD coronary heart disease CIN claim identification number CIPO Canadian Intellectual Property Office CLA conjugated linoleic acid CRAFT Co-operative Research Action for Technology (EU) CV Contingent Valuation CVD cardiovascular diseases DCE Discrete Choice Experiment DIN drug identification number DSHEA Dietary Supplement Health and Education Act (USA) EC European Commission EFSA European Food Safety Authority EHCR European Health Claims Regulation EU European Union FAO Food and Agriculture Organization (United Nations) FDA Food and Drug Administration (USA) FDAMA Food and Drug Administration Modernization Act FDR Food and Drug Regulations (Canada) FF functional food FFNet Functional Food Net (EU) FFNHP functional food and natural health product(s) (Canada) FNFC food with nutrient function claims (Japan) FOSHU food for specified health uses (Japan) FSANZ Food Standards Australia New Zealand (Agency) FSDU food for
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.577 | 0.580 |
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".