Modeling Distribution and Abundance of Soybean Aphid in Soybean Fields Using Measurements From the Surrounding Landscape
Bibliographic record
Abstract
Soybean aphid (Aphis glycines Matsumura) is a severe pest of soybean in central North America. Outbreaks of the aphid in Ontario are often spotty in distribution, with some geographical areas affected severely and others with few or no aphid populations occurring in soybean for the duration of the season. A. glycines spend summers on soybean and overwinter on buckthorn, a shrub that is widespread in southern Ontario and is commonly found in agricultural hedgerows and at the margins of woodlots. A. glycines likely use both short distance migratory flights from buckthorn and longer distance dispersal flights in the search for acceptable summer hosts. This study aims to model colonization of soybean fields by A. glycines engaged in early-season migration from overwintering hosts. Akaike's information criterion (AIC) was used to rank numerous competing linear and probit models using field parameters to predict aphid presence, colonization, and density. The variable that best modeled aphid density in soybean fields in the early season was the ratio of buckthorn density to field area, although dramatic differences in relationships between the parameters were observed between study years. This study has important applications in predicting areas that are at elevated risk of developing economically damaging populations of soybean aphid and which may act as sources for further infestation.
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 | 0.000 | 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) | 0.000 | 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".