Spreading Dogbane (<i>Apocynum androsaemifolium</i>) Development in Wild Blueberry Fields
Bibliographic record
Abstract
Spreading dogbane is a common perennial weed in wild blueberry fields. It is highly competitive and spreads rapidly once established. Herbicides can provide effective control of spreading dogbane, but application timing is important. The emergence pattern, ramet height, and flowering time of spreading dogbane were observed in 2008 and 2009, and thermal-based emergence, growth, and development models were developed and used to estimate optimum herbicide application timing. Spreading dogbane emergence and height were described with a three-parameter, sigmoid, nonlinear regression model, whereas flowering was described with a four-parameter, Weibull, nonlinear regression model. Spreading dogbane ramets initiated emergence soon after the biofix date of April 1. Peak emergence tended to occur at 420 growing degree days (GDD). Spreading dogbane reached its peak height by about 558 GDD. The maximum number of flowers per plant was reached at approximately 750 GDD. This study suggested that POST herbicides should be applied between 486 and 535 GDD to maximize efficacy. This time frame occurs after peak emergence and during early floral bud development.
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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 | 0.000 |
| 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 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".