Bibliographic record
Abstract
Surveillance Agency)/Brazil 83, 85 ANZTPA (Australia New Zealand Therapeutic Products Authority) 77 Ares et al. 147-8 Ares, G. 219-20 Argentina 230 Arias-Aranda, D. 207 Arnoult et al. 143-4 Arnoult, M.H. 220 Arvola, A 222 asymmetric information 33, 159 attitudes, consumer see consumer attitudes attributes, product 134-6, 150 Australia 21, 28, 80-81, 161, 163 consumer attitudes 221 definitions of health foods 16-17, 25 health claims 72-8, 168-9, 174, 181-2 regulatory regime 205 Australia New Zealand Food Standards Code 76 Australian Regulatory Guidelines for Complementary Medicines 77 bacteria cultures 110 BAFS (biologically active food supplements) 17-18, 25, 28, 86 Bailey, R. 114 bakery products 104-5 base product, attitude to 154 Bech-Larsen, T. 139-40, 192-3, 220-21 Belgium 142, 234-5 Bellavance, F. 227 benefits, health, types 116-17, 142-3, 233-4 CVD (cardiovascular diseases), reduction in 147, 153, 230 physiological 144, 231 beverages 104, 106, 110 bias, hypothetical 135 bioactive compounds 108 bioactive substances 17, 85 see also supplements bioengineered foods 52 biological role claims 56 see also nutrient function claims black market 86, 88 Blanchemanche, S. 228 Bleiel, J. 143, 208 blood glucose 95, 114, 171 blood pressure 7, 24, 49, 54, 73, 114, 172-3 reduction 90, 95, 171, 175 BNSFD (Bureau of Nutritional Sciences Food Directorate) 40, 193-4 body fat 95
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.831 | 0.747 |
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".