Methane and carbon dioxide emissions from yearling beef heifers and mature cows classified for residual feed intake under drylot conditions
Bibliographic record
Abstract
This study quantified methane (CH 4 ) and carbon dioxide (CO 2 ) production from beef heifers and cows classified for residual feed intake adjusted for off-test backfat thickness (RFI fat ) and reared in drylot during cold winter temperatures. Individual performance, daily feed intake, and RFI fat were obtained for 1068 crossbred and purebred yearling heifers (eight trials) as well as 176 crossbred mature cows (six trials) during the winters of 2015–2017 at two locations. A portion of these heifers (147 high RFI fat ; 167 low RFI fat ) and cows (69 high RFI fat ; 70 low RFI fat ) was monitored for enteric CH 4 and CO 2 emissions using the GreenFeed Emissions Monitoring (GEM) system (C-Lock Inc., Rapid City, SD, USA). Low RFI fat cattle consumed less feed [heifers, 7.80 vs. 8.48 kg dry matter (DM) d −1 ; cows, 11.64 vs. 13.16 kg DM d −1 ] and emitted less daily CH 4 (2.5% for heifers; 3.7% for cows) and CO 2 (1.4% for heifers; 3.4% for cows) compared with high RFI fat cattle. However, low RFI fat heifers and cows had higher CH 4 (6.2% for heifers; 9.9% for cows) and CO 2 yield (7.3% for heifers; 9.8% for cows) per kilogram DM intake compared with their high RFI fat pen mates. The GEM system performed at air temperatures between +20 and −30 °C. Feed intake of heifers and mature cows was differently affected by ambient temperature reduction between +20 and −15 °C and similarly increased their feed intake at temperatures below −15 °C. In conclusion, low RFI fat animals emit less daily enteric CH 4 and CO 2 , due mainly to lower feed consumption at equal body weight, gain, and fatness.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".