Study of persistency of lactation and survival of Iranian Holstein dairy cattle using random regression model
Bibliographic record
Abstract
The aim of this paper is to investigate whether characteristics of the first lactation (FL) curve of Iranian Holstein cows are associated with survival. Cows with least 10 test-days of milk production in their FL were used. The persistency of lactation (PL) and survival were estimated using a random regression model by restricted maximum likelihood with the ECHIDDNA software. We also used the Wood model to parameterize each individual lactation curve and then analyzed various curve characteristics using an animal model. The predicted breeding value (EBV) of the characteristics of the lactation curve of the cows from day 40 to 305 was predicted. The EBV of the production range (PR) and the slope of line in increasing phase ( m40,Peak) of production curve of sires with higher survival EBV were lower than other sires ( P < 0.05). The estimates of PL were independent of survival estimate. Therefore, the PR from 40th day after calving can be considered as a definition of PL because the lower the PR, the flatter is the milk production curve. Genetic evaluation of young bulls for survival needs the data of death or culling of their daughters. Therefore, the bulls can genetically be evaluated for survival according to the PL and m40,Peak of FL information of their daughters.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".