Using stable isotopes ( <scp> <i>δ</i> <sup>2</sup> H </scp> , <scp> <i>δ</i> <sup>13</sup> C </scp> ) to identify natal origins and larval host plant use by western bean cutworm, <i>Striacosta albicosta</i> (Lepidoptera: Noctuidae) captured in southern Ontario
Bibliographic record
Abstract
Abstract The Western Bean Cutworm ( Striacosta albicosta , Smith), a significant agricultural pest, has a broad distribution in North America having recently expanded northeastward. However, while there are significant populations each summer in Ontario, Canada, this species has limited local overwintering capacity, which suggests a high proportion of immigrant moths. Knowing the origins of immigrant moths would provide insight into their migration and could assist in their management. We used stable‐hydrogen isotope ( δ 2 H) analyses of wings from 283 moths captured over a seven‐year period (2012–2020) to estimate origins of these individuals based on the well‐established precipitation isoscape for the continent. We also analysed wings for stable‐carbon isotopes ( δ 13 C) to examine host plant use as this species uses both C3 (e.g., beans) and C4 (e.g., corn) sources during larval development. Regardless of year, most moths (71%–91%) fed on corn as larvae. We combined this finding with δ 2 H analyses to narrow probable geographic natal origins by applying an informed prior to Bayesian‐based isotopic assignments by assuming the Corn Belt of the United States as the likely region of origin. This combined approach indicated Iowa, Missouri, Illinois, Indiana and Ohio as the most likely origins, a conclusion supported by analyses of wind trajectories on nights prior to high trap catches in our study area. While our combined approach narrowed down the possible origins of cutworms captured in southern Ontario, estimates of origin remain relatively broad and to be useful for management purposes future refinements will be required.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".