Bibliographic record
Abstract
Abstract Domestication of farm animals begins 10 000 years ago. From a wide array of wild animals, only a small number were and, for specifiable reasons, could be domesticated. Trait selection and breeding has been the principal mechanism of animal improvement until the late twentieth century. An understanding of the genetic mechanisms and environmental factors in that process was a twentieth‐century triumph, which enhanced dramatically agricultural animal science. Drawing on physiological, behaviour, genetic, evolutionary and ecological knowledge, sophisticated mathematical models and high speed computing were employed to enhance significantly selection and breeding efforts. Late twentieth‐century agricultural biology began to employ techniques of molecular genetic manipulation (transgenic animals), initially using farm animals as bioreactors. Improvements in cloning, pro‐nuclear injection and use of stem cells can be expected to dominate research and development in twenty‐first‐century animal agriculture. Key Concepts The early evolution of animal domestication. Human and agricultural animal coevolution. Mechanisms of speciation. Quantitative genetics aspect of farm animal improvement. Critical variables in trait farm animal trait selection. Importance of multiple trait selection. Animals as pharmaceutical and materials bioreactors. Challenges and promises of cloning, pro‐nuclear injection and stem cell use in animal agriculture.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.025 |
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".