Increasing Use of Biocontrol in NYS Greenhouses with Producer-based Biocontrol Mentoring Network (Year 1)
Bibliographic record
Abstract
While biocontrol methods have been used successfully for insect control as a part of Integrated Pest Management (IPM) programs for many years, there is a low adoption of these practices by NYS greenhouse producers. Lack of experience, either directly or through discussion with a successful practitioner, limits the expectation of success, and therefore use of the procedures. This project is intended to create a group of producers with direct experience in biocontrol, based on a tour of floriculture greenhouses in Ontario that are successfully using biological control of insect pests. Tour participants will share this experience through a variety of outreach activities that will allow other growers to gain practical knowledge in using biological control in greenhouse crops; on-farm workshops for growers who implement biocontrol practices in their own greenhouses, presentations to industry organizations (Southern Tier Growers, the Green Industry Show, etc.) or to grower groups through Cornell Cooperative Extension meetings, educational materials that can be used by CCE or IPM, etc. By presenting the information to a broader group of producers than can attend the tour, the tour participants become mentors for the use of biocontrol in greenhouse crops in NYS, providing greenhouse growers with another tool in their pest control kit.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".