Bibliographic record
Abstract
Three production systems for sheep are modeled: spring, winter and accelerated lambing. Actual market price and lamb size over a seven‐year period (1992–98 inclusive) were used to determine which production/marketing system provides the most contribution margin,1 taking into account costs of production for each system. To determine the cost of production for each system, consensus meetings (a modified Delphi Method) are used to determine costs for 1998 and are then indexed back over the seven years using the Farm Input Price Index. Linear programming2 (contribution margin maximizing) computer models are designed. The results indicate that over the seven‐year period, one ewe would show a total contribution margin of $357 in a spring lambing system from a total of 9.80 lambs, $517 in a winter lambing system from 12.25 lambs, and $755 in an accelerated lambing system from 17.64 lambs. Although preference has shifted to larger lambs in the past five years, Christmas and Easter are by far the most important and preferred target markets for any size lamb. Trois systemes de production d'agneaux ont été modelés: agnelage d'automne, agnelage d'hiver et agnelage accéléré. Le prix du marché actuel et la taille des agneaux ont été résuméà travers une période de sept ans (1992–1998 inclusivement). Le prix et la taille ont donc été utilisé pour déterminer quel systéme de production/marketing fournirait la plus grande marge de contribution en ce qui a trait à la taille et le prix des agneaux ainsi que le coût de production. En 1998, plusieurs sessions de discussion ont eu lieu pour en arriver à une opinion commune sur le coût de production pour chaque système. Les prix ont alors été indexe pour les sept années précédentes. On a ensuite conçu des modèles d'optimisation (maximisation de la marge de contribution). Les résultats indiquent qu'il y a une préférence de poids des agneaux associéà des différents périodes au niveau du marché. On a également conclu que certains systèmes d'agnelage sont plus économiques que d'autres dans la production d'agneaux et ce, pour chaque marché.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".