Growing Degree-day Models for Predicting Lowbush Blueberry (Vaccinium angustifolium Ait.) Ramet Emergence, Tip Dieback, and Flowering in Nova Scotia, Canada
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Bibliographic record
Abstract
Experiments were established to evaluate the suitability of growing degree-day (GDD, T base = 0 °C) models for predicting emergence, tip dieback, and flowering of lowbush blueberry ramets in Nova Scotia, Canada. Data for model development were collected from quadrats established in several non-bearing and bearing blueberry fields throughout the dominant blueberry production areas in northern and central Nova Scotia. Blueberry ramets emerged between 222 and 265 GDD (6 May to 14 May) and reached 90% emergence between 619 and 917 GDD (7 June to 5 July). Emergence continued to slowly increase until late summer or early fall. Tip dieback began between 598 and 792 GDD (14 June to 21 June) and duration of this phase depended on whether late-emerging ramets developed to tip dieback. A four-parameter Weibull and a three-parameter Gompertz equation adequately explained cumulative blueberry ramet emergence and cumulative ramets at tip dieback as functions of GDD in the non-bearing year, respectively. The four-parameter Weibull function also explained the relationship between cumulative flowering ramets and GDD in the bearing year. Flowering ramets were first observed between 376 and 409 GDD (19 May to 30 May) in the bearing year. Model predictions for initiation of emergence, tip dieback, and flowering were 243, 692, and 389 GDD, respectively. Models were validated with independent data sets collected throughout northern and central Nova Scotia. The relationship between the percentage of open flowers on individual ramets and GDD in the bearing year was well described by a Gaussian model at two sites with a predicted peak number of open flowers between 552 and 565 GDD.
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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 | 0.001 | 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 | 0.001 |
| 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