Bibliographic record
Abstract
Angus cattle, 46 animal systems, 98±100 arable production systems, 79±94, 98 crops, 79±80, 86±94 area of, 81 yields, 86 farmyard manure and composting, 82±5 nutrients, 81±2 rotations, 79±80 weeds, 79, 80±81 Argentina, 6 Australia, 6, 123 Austria, 5, 128 dairy farming, 34 grassland in, 16 marketing organic produce in, 124 pig farming in, 61 poultry farming in, 78 sheep farming in, 50 available water capacity (AWC), 20 baby food, 123±4, 128 bacon, 69 banks, finance and, 112 barley, 79, 80, 88, 89, 90 basic slag, 21 beans, 90 Bioland, 159 body condition score (bcs) cattle, 47 sheep, 52 Bordeaux mixture, 91 bovine spongiform encephalopathy (BSE), 146, 168 break-even point, 119 British Organic Farmers and Growers, 145 British Organic Milk Producers (BOMP), 34 British Wool Marketing Brand, 54 Bulgaria, 10 butchers, 138 buyers, talking to, 127±8 cabbages, 94 Canada, 9 dairy farming, 35 cash flow, 109, 110±14during conversion to organic farming, 113±14 farm shops, 132 improving, 112±14 monthly checking, 111±12 catering outlets, 135±6 cattle, 16, 42±50, 99, 160±63 body condition score, 47 calving, 41±2, 44±5, 47±8 disease management, 49±50, 105 homoeopathy, 156±7 feeding, 35, 161
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.870 | 0.860 |
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".