Bibliographic record
Abstract
Summary The nutrient requirements of modern sows, and the availability of dietary nutrients for sows, are very poorly known in comparison to our knowledge of growing pigs. The number of published research papers in the last 40 years on growing pig nutrition is in the tens of thousands, however, there are only about 800 publications on sow nutrition listed in the Commonwealth Agricultural Bureau database – less than 1% of all publications concerning pigs. If we still don’t know everything we need to about how to feed growing pigs – imagine what we don’t know about sows! The productivity of sows has increased dramatically in the last 20 years, however, the research, upon which current dietary recommendations are based, dates from the late 1970’s to the early 1990’s (ARC 1981, NRC 1998). In addition, many of the nutrient recommendations for sows are unverified extrapolations from research in growing pigs. Our recent research shows that the current recommendations for both energy and amino acid intake in sows (NRC 1998) are incorrect by a significant margin. The economic benefit to producers of research to revise and update the energy and amino acid requirements of sows is estimated to be worth in excess of $4.50 per pig marketed (Grier et al 2006).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".