An Integrated Pest Management Adoption Survey of Sweet Corn Growers in the Great Lakes Region
Bibliographic record
Abstract
Sweet corn is one of the most common fresh market vegetable crops grown throughout the north central and north east regions of the United States. In 2008, the Great Lakes Vegetable Working Group measured integrated pest management (IPM) practice adoption by growers of this crop using online and hardcopy surveys over a 10-mo period. The survey asked growers from nine states and Ontario, Canada, which pest management practices they used on their farm operation in the following sections: education, preplant, at-plant, in-season, postharvest, scouting, and demographics. Each individual survey question was ranked by a panel of university specialists and designated as a low, moderate, or high IPM valued activity, with points assigned accordingly. On survey completion, the total points accumulated by the grower would place them into one of three categories; low, moderate, or high IPM adopter. Of the 407 respondents, 130 were placed in the low IPM adoption category, 251 were deemed moderate IPM adopters, and 26 were placed in the high IPM category. Some key general attributes of a high IPM adopter include someone who has grown vegetables for at least 10 yr and has a farm >51 acres (67%) and raises between 21-50 acres of sweet corn (44%). Some key general attributes of a low IPM adopter include less experience on smaller acreage, with 56% having grown vegetables for fewer than 10 yr with 57% on farms smaller than five acres.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".