An assessment of degree-day models to predict the phenology of alfalfa weevil (Coleoptera: Curculionidae) on the Canadian Prairies
Bibliographic record
Abstract
Abstract This study examined the use of degree-day models to predict alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae) population development on the Canadian prairies. Air temperatures, alfalfa weevil abundance, and instar data were collected in 2013 and 2014 from 13 alfalfa (Medicago sativa Linnaeus; Fabaceae) fields across Alberta, Saskatchewan, and Manitoba. We coupled three alfalfa weevil population prediction models with three temperature data sources to determine which combination most closely aligned with results observed. Our objective was to find the best prediction of peak occurrence of second instar alfalfa weevils, the optimum time for management decisions. Of the parameters analysed, prediction model had the greatest effect on the accuracy of peak instar prediction, with Harcourt and North Dakota models better at predicting population peaks than the Guppy–Mukerji model. Interactions between temperature source and prediction model significantly affected prediction accuracy. The probability of accurate prediction of population peaks to within 3.5 days of actual occurrence using in-field and multiple-site temperature data sets, combined with Harcourt and North Dakota development models, was 0.45–0.70. Lower predictability was found from fields in the Mixed Grass Ecoregion than in other ecoregions. The use of the recommended models can assist growers in timing their monitoring activities and deciding if pest management action is warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".