Predicting Developmental Timing for Immature Canada Thistle Stem-Mining Weevils,<i>Hadroplontus litura</i>(Coleoptera: Curculionidae)
Bibliographic record
Abstract
Predictions of phenological development for insect biological control agents may facilitate post-release monitoring efforts by allowing land managers to optimize the timing of monitoring activities. A logistic thermal time model was tested to predict phenology of immature stem-mining weevils, Hadroplontus litura F. (Coleoptera: Curculionidae), a biological control agent for Canada thistle, Cirsium arvense L. (Asterales: Asteraceae). Weevil eggs and larvae were collected weekly from Canada thistle stems in eastern North Dakota from May through July during 2010 and 2011. Head capsule widths of sampled larvae were measured at the widest point and plotted on a frequency histogram to establish ranges of head capsule widths associated with each instar. We found head capsule width ranges for first-, second-, and third-instar H. litura larvae were 165-324 µm, 346-490 µm, and 506-736 µm, respectively. Logistic regression models were developed to estimate the proportions of H. litura eggs, first-, and second-instar larvae in the weevil population as a function of thermal time. Model estimates of median development time for eggs, first instars, and second instars ranged from 219 ± 23 degree-days (DD) to 255 ± 27 DD, 556 ± 77 DD to 595 ± 81 DD, and 595 ± 109 DD to 653 ± 108 DD, respectively. Based on model validation statistics, model estimates for development timing were the most accurate for eggs and first instars and somewhat less accurate for second instars. These model predictions will help biological control practitioners obtain more accurate estimates of weevil population densities during post-release monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".