Inconsistent food safety pressures complicate environmental conservation for California produce growers
Bibliographic record
Abstract
Controlling human pathogens on fresh vegetables, fruits and nuts is imperative for California growers. A range of rules and guidelines have been developed since 2006, when a widespread outbreak of E. coli O157:H7 was linked to bagged spinach grown in California. Growers face pressure from industry and government sources to adopt specific control measures on their farms, resulting in a complex, shifting set of demands, some of which conflict with environmental stewardship. We surveyed 588 California produce growers about on-farm practices related to food safety and conservation. Nearly all respondents considered both food safety and environmental protection to be important responsibilities for their farms. Responses indicate that clearing vegetation to create buffers around cropped fields, removing vegetation from ditches and ponds, and using poison bait and wildlife fences are commonly used practices intended to reduce wildlife movements onto farm fields. The survey also revealed that on-farm practices vary substantially even among farms with similar characteristics. This variability suggests inconsistencies in food safety requirements, auditors’ interpretations or growers’ perception of the demands of their buyers. Although site-specific considerations are important and practices should be tailored to local conditions, our findings suggest growers, natural resources and food safety would benefit from clearer, more consistent requirements.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".