Analysing the links between agriculture and climate change: can 'best management practices' be responsive to climate extremes?
Bibliographic record
Abstract
Rural communities the world over depend on agriculturally-based livelihoods. In the Canadian prairies, access to sufficient quality and quantity of water can be challenging. Agriculture is fundamentally susceptible to access to water during critical crop germination and growth periods. Climate change models for the Canadian prairies indicate, in general, that summer growing seasons will experience less frequent, but larger precipitation events. The anticipated results of a changing climate include more frequent spring flooding and a new climate regime that requires more proactive water management to ensure availability of adequate supplies at optimal times to support and sustain agricultural production. Multi-disciplinary research is investigating, quantifying, and critically assessing currently purported beneficial management practices (BMPs) for agriculture to determine rural community vulnerability and adaptability to climate change. The presentation includes research results from field scale implementation and testing of BMPs, interviews of rural communities and residents, and quantitative evaluations of rural economies, development, and adaptation strategies.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".