147 Use of Productivity Enhancing Technologies in Beef Steers Reduces Greenhouse Gas Emission Intensity
Bibliographic record
Abstract
Abstract In the last decade, the cattle industry has witnessed increased demand from domestic and international markets for hormone and antibiotic free beef. However, the environmental consequences of switching from conventional production to “natural” beef have not been extensively examined. This study explored greenhouse gas (GHG) emissions estimated using a whole-farm model, Holos (www.agr.gc.ca/holos-ghg) with model inputs from a 2-year replicated study in which calves were managed from weaning to finishing with 6 treatments based on body weight and feeding strategy. Treatments included Heavy (H), which were directly finished, Medium (M), which were backgrounded prior to finishing, and Light (L), which were backgrounded and then grazed during the summer before being placed on a finishing diet. Each treatment was subdivided into two management practices: Conventional (CON) in which productivity enhancing technologies (PETs) were used (ionophores, steroid implants and beta-adrenergic agonists) and Natural (NAT) in which PETs were removed. Emission intensity (kg CO2e kg-1 boneless beef) of HNAT was 17.3 to 20.1% higher than HCON, MNAT was 20.3 to 20.5% higher than MCON and LNAT were 11.5–12.6% higher than LCON treatment. Conventional treatments, using PET’s, reduced GHG emissions, however grazing should be considered for its potential to store and promote carbon sequestration in perennial grasslands.
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 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 | 0.001 | 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".