Investigating the Carbon Footprint of Cattle Grazing the Lac du Bois Grasslands of British Columbia
Bibliographic record
Abstract
Greenhouse gas emissions from cattle have been increasingly recognized as an important anthropogenic source. We investigated the impact of cattle ranching on these emissions in British Columbia in order to determine the overall carbon footprint. The grazing activity within the Lac du Bois grasslands of British Columbia was examined, with emphasis on identifying point sources and removals of greenhouse gas emissions from cattle ranching. Enteric methane emissions were empirically measured at two elevation gradients in the spring and fall of 2010. Cattle emitted on average 370 L CH4 per day; these measurements on native grasslands are comparable to work on tame pastures. A life cycle analysis was conducted with a validated HOLOS model based on empirical measurements. The following grassland improvement strategies were evaluated: reducing stocking density; and reseeding/interseeding grass and legumes with and without synthetic fertilizer additions. Reseeding was the most effective at reducing the carbon footprint of cattle ranching on the Lac du Bois grasslands. Reseeding initiatives could theoretically result in soil carbon sequestration rates of 2.12 Mg CO2 equivalent per hectare. A combination of reductions and removals should be implemented in the future to reduce the overall carbon footprint of cattle ranching in British Columbia.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".