Development of organisation and planning in animal breeding: II. A review on breeding planning
Bibliographic record
Abstract
Abstract. Breeding planning resp. planning of breeding measures as scientific discipline within animal breeding science has developed in close connection to quantitative genetics in the middle of the last century. From the beginning, breeding planning was mainly focussed on macroeconomic resp. national targets. This viewpoint has been maintained until today. This is in conflict with existing business oriented organisational structures in animal breeding. The present study gives an overview on the development of breeding planning. Based on five recent works on breeding planning it is shown that approaches have already been developed that allow for taking micro- and macroeconomic aspects into account when looking into the organisational structures. Building on these examples, the study aims at working out further approaches to breeding planning based on enterprise resource, supply chain and value added chain planning. Breeding planning combined with consideration of organisational structures and, where necessary, with organisational analysis allows to take the requirements of the private and the public sector into account. Thereto new research fields open up within animal breeding science.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".