Traceability of Fruit Production Practices Enhanced by Trac Record-keeping and Reporting Software
Bibliographic record
Abstract
Trac Software, an Excel-based record-keeping and reporting software program, enables fruit farmers to easily maintain and report accurate crop protection records that are, 1) vital to their market edge, when increasingly competitive global markets demand detailed pesticide records and product traceability, and 2) critical to their IPM practices, especially when faced with pest or disease control failures and severe outbreaks. Trac Software was upgraded in 2006 for all fruit crops commonly grown in New York and 330 CDs distributed. Trac Software support materials were also updated including a Software Manual, a Getting Started guide, and a comprehensive website. In 2006, the software was programmed for adding more rows to the forms, sending information to specific processor reports, and filtering processor reports. In 2006, Trac Software development began for turfgrass, for fruit farmers in Ontario, Canada, and for the EcoApple report form of Red Tomato. A Trac survey of 253 recipients is underway.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".