Genetic and phenotypic analysis for profitability in Iranian Holsteins
Bibliographic record
Abstract
The objectives of this study were to assess profit and to document phenotypic and genetic trends for profit in Holstein dairy cows of Iran. A total number of 219 507 first lactation cows with performance records from 569 herds were used to calculate profit per cow per year. Economic data sources used for calculations were from three large dairy farms representing the marketing circumstances of Iran during the 12 yr study period (2000–2011). Variance components were estimated using the average information restricted maximum-likelihood procedure based on an animal model. An average cow of the population had its first calving at 25.4 mo of age, produced 7616.9 kg of milk in its first lactation, and generated a net profit of $1096.20 US per year. Profit calculated as a phenotype for each individual cow had a moderate heritability of 0.22, but our data did not support profit as an alternative to selection based on an economic selection index. The phenotypic and genetic trends for profit were −$44.43 US and $5.33 US per cow per year, respectively. The genetic trend was linear, whereas the phenotypic trend showed two peaks and three valleys. Our results show that in spite of an undesired phonotypic trend for profit driven by fluctuations in the Iranian economic circumstances, the genetic trend was favorable and can be attributed to the importation of semen from western countries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".