Agriculture in a changing climate: Learning from the east Canadian situation
Bibliographic record
Abstract
The impact of climate change on agriculture differs depending on the region and sector of activity. Predictive models suggest that climate change in eastern Canada will overall result in increased temperatures, changed precipitations patterns, and overall longer cropping seasons. Both modelling and actual experimentation in controlled environments and in fields suggest that yield response will vary depending on the crop species. In cool-season forage species, which are the predominant in the region, yields are expected to increase while the nutritive value is expected to be negatively affected. Changes in precipitation patterns and increased temperatures in the winter may jeopardize the winter survival of some perennial species. Increased temperature will, however, expend the area in which some warmer-season crops such as corn may be grown locally. The development of climate-smart approaches to develop resilient agricultural production systems and technologies are currently being researched locally. It is a concerted effort that includes changes in policies, adaptation of field management practices, the local introduction of new crop species, selection of new traits associated with abiotic stress resistance, and the development of new technologies that can help local crops cope with stresses associated with climate change. This presentation will review some of the challenges and opportunities associated with climate change in eastern Canada and some of the local initiatives to adapt to this changing climate with a focus on forage crops.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".