PSX-B-19 Behaviour of cows while being tested for methane emissions using the greenfeed system
Bibliographic record
Abstract
Abstract Increased focus on sustainability is driving a need for environmental efficiency traits in dairy cattle breeding. Breeding for reduced emission of methane, an inevitable product of fermentation in ruminants, is increasingly being explored. Methods to measure methane emissions vary but can be impacted by cow behaviour. As part of an on-going project to develop genomic tools for breeding resilient dairy cows, we explored changes in cow behaviour over time during methane emission measurements. First lactation heifers (n = 49) were tested in tie-stall housing at a research herd in Ontario, Canada. Animals were tested over 5 consecutive days at 08:00h, 12:00h, and 16:00h each day for a 10-min period using the GreenFeed system (C-Lock Inc., Rapid City, SD, USA). The frequency of movement (body shifts and leg lifts) and the number of seconds the cow removed her head from the machine were recorded. The effect of day on the average frequency of movements or time the cow’s head was outside of the machine was assessed using a repeated measures model. In general, cows moved their legs the most on day 1 of testing (76 ± 5.0 movements per 10 min), after which it numerically decreased (e.g., day 5: 68 ± 5.0 movements per 10 min, P = 0.1110). A similar effect was observed for seconds the cow had her head out of the machine (P = 0.0650). Cows spent an average of 39 ± 5.7 sec with their head outside of the machine on day 1 versus 25 ± 3.6 sec on day 5 (P = 0.0499). These preliminary results suggest that cows adapt to the testing conditions; however, changes in their behaviour were minor and do not intervene with recording of methane emissions using the GreenFeed system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".