Predicting major peach yield reductions in the Midwest and Southeast United States
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".