Models for Estimating the Cold Hardiness of Sweet Cherry (Prunus avium cv. Sweetheart and Lapins) in Cold Climate Regions
Bibliographic record
Abstract
Plant cold hardiness is a dynamic process, and seasonal changes occur through cold acclimation and deacclimation to help prevent lethal injury from the cold. Cold weather injury resulting from inadequate plant cold hardiness can result in significant economic losses to growers of perennial crops in temperate climates. The objective of the current study was to develop models that estimate the lethal temperature that causes 10%, 50%, and 90% mortality (LT10, LT50, LT90) to two cultivars of sweet cherry ( Prunus avium ) flower buds from the early fall through to spring. We parameterized regression models using lethal temperature data collected in the Okanagan Valley, British Columbia, Canada, over six seasons (2013–17, 2019–20, 2021–22) for ‘Sweetheart’ sweet cherry and three seasons (2013–15, 2016–17) for ‘Lapins’ sweet cherry. These models incorporate parameters that are based on equations that describe chill and heat accumulation that rely on measures of hourly air temperature. Model evaluation and validation using several seasons of lethal temperature data not included in model development were completed. Models for estimating the cold hardiness of sweet cherry showed good agreement between model lethal temperature predictions and observed values for both sweet cherry cultivars. In addition, an open-access, interactive, web-based application was developed to access the outputs of these models in real time for use by growers, researchers, and extension workers. These current models of sweet cherry cold hardiness have potential application for use as a decision support tool for cold damage management as well as crop site suitability modeling.
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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 | 0.001 | 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 | 0.000 | 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".