Bibliographic record
Abstract
Modelling Animal SystemsAgricultural scientists have, for well over a century, sought mathematical descriptions of how animals go about their business of nutrition, growth and reproduction.The early issues of the Journal of Agricultural Science reflect this interest.Gavin in 1913 attempted to describe milk yield using regression coefficients (5, 377-390).In 1914, Wood and Yule stressed the importance of predictive accuracy in animal nutrition (6, 233-251) and, in 1915, Murray highlighted the need for formulae in determining nutrient requirements (7, 154-162).Ever since these early years, the Journal has continued to publish mathematical modelling papers concerned with aspects of animal agriculture.Not only full papers but also conference abstracts, having first carried the Proceedings of the Agricultural Research Modelling Group in 1990 (115, 145-149).This special issue is dedicated to modelling animal systems papers to mark nearly a century of Journal involvement in this field.The theme will be continued in subsequent issues of the Journal throughout 2008.The papers published under this rubric are concerned with modelling animal process in their broadest sense.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".