The role of livestock in sustainable food production systems in Canada
Bibliographic record
Abstract
Global drivers such as the growing human population, evolving consumer preferences, globalization, and climate change have put pressure on the agri-food sector to produce more livestock products with less land, feed, and water. Taste, nutritional value, cost, convenience, source, animal welfare, and environmental sustainability of food are criteria upon which purchasing decisions are made. In response, an environmental footprint analysis composed of greenhouse gas emissions, nutrient and water use efficiency, water quality, carbon storage, and biodiversity has been completed for many commodities. However, as livestock production systems occur within complex agro-ecosystems, it is extremely challenging to formulate a single overall sustainability metric. There is no “silver bullet” to solve the environmental concerns of all livestock production systems as they operate under different constraints on different landscapes, with different water and nutrient cycles, and soil types. Furthermore, the lack of scientific evidence regarding the interactions between livestock production, human nutritional adequacy, and the health of our environment makes it difficult for consumers to interpret this information and make informed food choices. This review examines these complex interactions and trade-offs, as well as the potential impacts of changes in consumer dietary choice on environmental sustainability, nutritional adequacy, and land use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".