Bibliographic record
Abstract
Enteric methane emission from ruminants contributes substantially to the greenhouse effect. Few studies have focused on the genetic variation in enteric methane emission from dairy cattle. One reason for that is the limited number of methods appropriate for large scale phenotyping to measure a sufficient number of animals available to estimation of additive genetic variance. A method to measure methane in dairy cattle using a Fourier Transformed Infrared (FTIR) approach during milking in Automatic milking systems was implemented by Lassen et al . (2012). Such data showed repeatability estimates around 0.40 for the ratio between methane and carbon dioxide concentrations. Using the ratio between methane and carbon dioxide as a phenotype makes it possible to quantify the amount of methane produced per cow, because the amount of carbon dioxide can be estimated from variables such as weight, milk production and feed intake (Madsen et al ., 2010). In a study of 548 heifers a heritability estimate of 0.35 was obtained for predicted methane emission based on registrations on feed intake rather than on direct measurements (de Haas et al., 2011). Estimates of this magnitude justify the use of genetic tools to reduce methane emission from dairy cattle. Another study (Wall et al ., 2010) has shown that selecting for correlated indicator traits such as productivity and efficiency would help lowering the methane emission from the cattle production. Furthermore, it is still important to have emphasis on production traits through use of a total merit indexes to avoid a decline in economically important traits when reducing methane emission. However, key genetic parameters are still inaccurate and would therefore benefit from being re-estimated on larger numbers of animals and records based on reliable direct methods. The objective of this study was to estimate the heritability for enteric methane emission from Danish Holstein cows using a non-invasive method
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.823 | 0.740 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".