Methane and carbon dioxide emissions and grazed forage intake from pregnant beef heifers previously classified for residual feed intake under drylot conditions
Bibliographic record
Abstract
The objectives of this study were to quantify the effect of post-weaning residual feed intake (RFI) on subsequent grazed forage intake, methane (CH4), and carbon dioxide (CO2) emissions. Beef heifers classified for RFI adjusted for off-test backfat (RFIfat; 55 high and 56 low) at 9 mo of age were monitored 7 mo later for CH4 and CO2 emissions using the GreenFeed Emissions Monitoring system. About 56 of these heifers were also monitored as high and low RFIfat groups using open-path Fourier-transform infrared spectroscopy (OP-FTIR). Heifers were dosed with 1 kg of C32-labelled pellets once daily for 15 d, with twice daily fecal sampling the last 8 d to determine individual grazed forage intake using the n-alkane method. Low RFIfat pregnant heifers consumed less forage (10.25 vs. 10.81 kg dry matter d−1; P < 0.001), and emitted less daily CH4 (238.7 vs. 250.7 g d−1; P = 0.009) and CO2 (7578 vs. 8041 g d−1; P < 0.001) compared with high RFIfat animals. Results from the OP-FTIR further confirmed that low RFIfat heifers emitted 6.3% less (g d−1; P = 0.006) CH4 compared with their high RFIfat cohorts. Thus, selection for low RFIfat will decrease daily CH4 and CO2 emissions from beef cattle.
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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 | 0.000 |
| 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".