Estimating yield response to temperature and identifying critical temperatures for annual crops in the Canadian prairie region
Bibliographic record
Abstract
Robertson, S. M., Jeffrey, S. R., Unterschultz, J. R. and Boxall, P. C. 2013. Estimating yield response to temperature and identifying critical temperatures for annual crops in the Canadian Prairie region. Can. J. Plant Sci. 93: 1237–1247. Historical yield and temperature data, by municipal district for the three prairie provinces, are used to estimate the marginal effect of exposure to specific temperatures in defined ranges during the growing season. Incorporating these non-linear temperature effects into the model improves yield forecasting for Canadian prairie annual crops over models that use average temperatures or growing degree days. Critical maximum temperatures at which yields decline, calculated for winter wheat, spring wheat, durum wheat, barley, spring rye, fall rye, oats, canola and flax, range between 28 and 34°C, depending on the crop. Additional critical minimum and maximum temperatures are estimated using the marginal effect of exposure to specific temperatures in defined ranges. Estimates of critical maximum temperatures and their marginal impact on yields are important for research and policy analysis on various issues and problems, including climate change, risk management instruments such as crop insurance, and development of heat tolerant crop varieties.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".