Spatio-temporal patterns of extreme weather events and their impacts on corn (Zea mays) and soybeans (Glycine max) in eastern Ontario
Bibliographic record
Abstract
Extreme weather events have multiple adverse effects, with sectors like agriculture being particularly vulnerable to their impacts. Changes in weather extremes in eastern Ontario from 1961 to 2010 were investigated by assessing trends in extreme event indicators. In addition to generic and agroclimatic indicators, corn- and soybean-specific indices were developed, accounting for crop tolerances to extremes at different growth stages. A shift to a warmer, wetter climate and increases in accumulated heat units and growing season length were observed. Flooding during soybean planting season and early stages of corn development became more prevalent. Additionally, increases in drought events during critical reproductive stages were recorded for both crops. The most significant changes occurred in areas where key agricultural lands are located. The results of this research will help to identify opportunities and threats to crop production, make informed decisions on modifying agricultural practices and develop tools to support adaptive policy development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".