Predicting major peach yield reductions in the Midwest and Southeast United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many fruit crop failures, including those for peaches, are caused by extremely low winter temperatures or by false springs, which is when a hard freeze occurs in the spring after plants have broken dormancy and started to grow. A decision‐support tool was created to predict major, regional peach yield reductions based on the analysis of significant peach crop loss years between 1934 and 2016 in the Midwest (Illinois, Missouri and Arkansas) and Southeast (Alabama, Georgia, South Carolina and North Carolina) regions of the United States using surface temperature data. The tool was tested using data from high‐yield peach years and was found to function well in all the sample years for the Midwest, but only for 75% of years for the Southeast. The tool was then tested on the 2017 false spring event that occurred over parts of the Eastern United States. The tool correctly indicated that the entire Southeast region would likely experience a major peach crop yield reduction, while many peach‐growing areas in the Midwest were spared as not all Midwest stations had accumulated enough growing degree‐days before experiencing a hard freeze. Composite 500 hPa geopotential height anomalies associated with the “warm” periods of false spring events were 100 m above average for the Midwest, and 100–125 m for the Southeast. Cold period composites of the low‐yield years suggested 500 hPa geopotential height anomalies were 100–200 m below average for the Midwest, and 100–175 m for the Southeast. The decision‐support tool will assist the peach industry to anticipate major, regional yield reductions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it