Temperature Thresholds and Growing-Degree-Day Models for Red Sorrel (<i>Rumex acetosella</i>) Ramet Sprouting, Emergence, and Flowering in Wild Blueberry
Bibliographic record
Abstract
Red sorrel is a common herbaceous creeping perennial weed in wild blueberry fields in Nova Scotia that spreads by seeds and an extensive creeping root system. Experiments were established to determine temperature thresholds for ramet sprouting from creeping root fragments and to develop growing-degree-day (GDD) models for predicting ramet emergence and flowering under field conditions in wild blueberry fields in Nova Scotia. Ramets sprouted from root fragments at temperatures as low as 1 C, with an optimum temperature for ramet sprouting around 22 C. Ramet sprouting was completely inhibited at temperatures above 35 C. Cumulative ramet emergence and flowering under field conditions were adequately explained as functions of GDD by a three-parameter power equation ( R 2 = 0.98) and a four-parameter logistic equation ( R 2 = 0.87), respectively. Ramet emergence began between 110 and 265 GDD and continued throughout the season at each site. Model prediction for the initiation of emergence was 92 GDD, and 50 and 95% emergence were predicted to occur at 1,322 and 2,696 GDD, respectively. Red sorrel ramets began to flower in the field between 308 and 515 GDD. Model prediction for the initiation of flowering was 289 GDD, and 50 and 95% flowering were predicted to occur at 545 and 1,336 GDD, respectively. Model validation was conducted with the use of two additional independent data sets for emergence and flowering and indicated good performance of the proposed models ( R 2 and root-mean-square error values ranging from 0.96 to 0.99 and 4.0 to 13.8, respectively). The models allow for direct comparison of red sorrel phenology to that of the wild blueberry and will aid in the development of new management strategies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".