Bibliographic record
Abstract
A total of 95,678,311 feed records from January 2000 to May 2007, corresponding to 16,866,117 test-day records or 1,714,651 cow lactation records obtained from Quebec Dairy Herd Improvement program, VALACTA were used to estimate heritabilities of feed intake. Genetic parameters of feed intake were estimated using both a single and two-trait (with milk yield as the second trait) animal models which included herd-year-season (hys) at calving, age at calving, male and female phantom groups as fixed effects, and animal and residual as random effects (i.e. for analysis of complete lactation feed intake in parity 1, there were 119,620 cows from 1,248 sires and 88,500 dams, 20,133 levels of hys, 20 levels of age at calving and 49 phantom groups). The model was fitted by Restricted Maximum Likelihood and relationships among animals were taken into account on both the male and female side of the pedigree (i.e. for analysis of complete lactation feed intake in parity 1, there were 308,029 animals from 6 generations in the pedigree file). Heritability estimates obtained from the single trait model ranged from 0.04 to 0.14 for complete lactation feed intake traits, 0.01 to 0.03 for 90-d feed intake traits, 0.08 to 0.19 for 305-d feed intake traits, and 0.10 to 0.21 for daily feed intake traits. Heritability estimates for 305-d dry matter intake obtained from the two-trait model were 0.06, 0.01 and 0.08 in parity 1, 2 and 3 respectively.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".