Factors associated with winged forms of soybean aphid and an examination of <scp>N</scp> orth <scp>A</scp> merican spatial dynamics of this species in the context of migratory behaviour
Bibliographic record
Abstract
Abstract Soybean aphid is a severe invasive pest of soybeans in N orth A merica. From 2005 onward, a network of 47 suction traps in N orth A merica, each approximately 7 m in height, has monitored aerial populations of this species. Suction trap captures of alate viviparous Aphis glycines in summer, and gynoparae and males in the autumn, were subjected to negative binomial regressions with environmental parameters. Models were ranked by using information‐theoretic methods to determine which parameters best explained aerial density of aphids. Multiparameter models incorporating the best parameters were constructed. Best‐fit models were used to compute peak values of each parameter, where a quadratic relationship between captures and that parameter occurred. Data were subjected to non‐metric multidimensional scaling and analysis of similarity to observe patterns in the geographical and temporal distribution of captures. Summer flight activity A. glycines was explained best by the level of aphid infestation in fields local to the trapping site, although the autumn female flight activity peak and male activity was best explained by photoperiod. Seven‐metre tall suction traps are useful for determining when flight activity of A. glycines occurs within a region.
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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 | 0.000 |
| 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) | 0.002 | 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".