Growing Degree-day Models for Predicting Lowbush Blueberry (Vaccinium angustifolium Ait.) Ramet Emergence, Tip Dieback, and Flowering in Nova Scotia, Canada
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".