PSXI-23 Modelling environmental impacts associated with use of implants in pre-weaned calves.
Bibliographic record
Abstract
Abstract The performance and environmental sustainability advantages of implanting backgrounded and finished cattle have been well documented. The objective of this research is to explore the environmental sustainability of implanting pre-weaned calves in Canada. Greenhouse gas (GHG) emissions, and land and water use intensities were modeled in a cow-calf to finish system in which pre-weaned steer calves were raised with (IM) or without (NIM) implants. Pre-weaned calves (n=130) received Synovex C at 30 days of age while the remaining 130 steers were not implanted. Greenhouse gas emissions (including enteric and manure CH4, direct and indirect N20 and energy C02) were calculated using the Holos model for three eco-regions in Manitoba, Canada. Land and water use were calculated using spreadsheet models. Due to the increased liveweight (LW) of IM compared to NIM calves at weaning (255 vs 245 kg), GHG emission intensity (kg CO2e kg LW-1) was 3.79% lower for the former. Land use (ha kg LW-1) and water use (L kg LW-1) intensities were also lower by 3.86% and 3.92%, respectively. In addition, estimated revenue from IM calves at weaning was $94.10 greater per calf compared to NIM calves. Using hormonal implants in pre-weaned steers reduced the environmental footprint and generated greater economic returns than those not implanted before weaning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".