An Energy Balance Model for Predicting Berry Temperature and Scheduling Sprinklers for Cooling in Northern Highbush Blueberry
Bibliographic record
Abstract
Heat-related fruit damage is a prevalent issue in northern highbush blueberry ( Vaccinium corymbosum L.) in various growing regions, including the northwestern United States. To help address the issue, we developed a simple climatological model to predict blueberry fruit temperatures based on local weather data and to simulate the effects of using over-canopy sprinklers for cooling the fruit. Predictions of fruit temperature on sunny days correlated strongly with the actual values ( R 2 = 0.91) and had a root mean-square error of ≈2 °C. Among the parameters tested, ambient air temperature and light intensity had the greatest impact on fruit temperature, whereas wind speed and fruit size had less impact, and relative humidity had no impact. Cooling efficiency was estimated successfully under different sprinkler cooling intervals by incorporating a water application factor that was calculated based on the amount of water applied and the time required for water to evaporate from the fruit surface between the intervals. The results indicate that water temperature and nozzle flow rate affected the extent to which cooling with sprinklers reduced fruit temperature. However, prolonging the runtime of the sprinklers did not guarantee lower temperatures during cooling, because cooling efficiency declined as the temperature of the fruit approached the temperature of the irrigation water. Users could incorporate the model into weather forecast programs to predict the incidence of heat damage and could use it to make cooling decisions in commercial blueberry fields.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".