Growing degree-day models for predicting narrowleaf goldenrod ( <i>Euthamia graminifolia</i> ) phenology in lowbush blueberry fields in Nova Scotia
Bibliographic record
Abstract
Abstract Narrowleaf goldenrod [ Euthamia graminifolia (L.) Nutt.] is the most common goldenrod species in lowbush blueberry ( Vaccinium angustifolium Aiton) fields in Nova Scotia, Canada. Knowledge of ramet emergence and phenological development of this weed is limited, and it is unknown if seedling emergence contributes to the maintenance of established populations. The objectives of this research were to (1) develop predictive GDD models for E. graminifolia ramet emergence and phenological development, (2) determine whether E. graminifolia forms seedbanks in lowbush blueberry fields, and (3) establish whether E. graminifolia seedlings emerge in lowbush blueberry fields. Cumulative E. graminifolia ramet emergence was explained as a function of GDD using a four-parameter Weibull equation that predicted emergence to begin at 72 GDD and 90% emergence to occur at 458 GDD. Cumulative ramets at the flower bud and flowering stages were explained as a function of GDD using a three-parameter Gompertz equation that predicted initiation of the flower bud and flowering stages at 644 and 1,369 GDD, respectively, and 90% of ramets at the flower bud and flowering stages at 1,522 and 2,113 GDD, respectively. Cumulative E. graminifolia seedling emergence ranged from 2.4 ± 0.8 to 4 ± 1 seedlings m −2 , suggesting limited seedling emergence in lowbush blueberry fields. Seedling density from soil core samples, however, ranged from 38 ± 25 to 10,940 ± 1,456 seedlings m −2 . These results suggest that E. graminifolia forms seedbanks in lowbush blueberry fields, despite the low levels of seedling emergence observed. Euthamia graminifolia seedling management should therefore be considered in current weed control programs, and growers can use the developed GDD models to aid the management of established plants.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".