Weed Survey of Nova Scotia Lowbush Blueberry ( <i>Vaccinium Angustifolium</i> Ait.) Fields
Why this work is in the frame
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Bibliographic record
Abstract
Weed surveys provide the basis for weed management research in lowbush blueberry, but have not been conducted in Nova Scotia since 2001. Documented declines in herbicide efficacy, loss and/or acquisition of herbicide active ingredients, confirmation of herbicide-resistant weed biotypes, and documented vectoring of weed seeds by machinery necessitate a new weed survey. A total of 165 bearing year lowbush blueberry fields were surveyed from 2017 to 2019, within which approximately 211 weed species were identified. Most weed species were herbaceous perennial forbs (89 species) and woody perennials (50 species), followed by annual broadleaf (24 species) and perennial grass weeds (20 species). The remaining flora consisted of a range of ferns, biennials, sedges and rushes, and orchids. The most common weed species were red sorrel (Rumex acetosella L.), poverty oatgrass (Danthonia spicata L. Beauv.), haircap moss (Polytrichum commune Hedw.), hair fescue (Festuca filiformis Pourr.), narrow-leaved goldenrod (Euthamia graminifolia (L) Nutt.), tickle grass (Agrostis hyemalis (Walter) BSP.), woolly panicum (Panicum lanugosum Ell.), cow wheat (Melampyrum lineare Desr.), bunchberry (Cornus canadensis L.), and yellow hawkweed (Hieracium caespitosum Dumort). Increased occurrence of these weed species is likely the result of documented or observed reductions in hexazinone and terbacil efficacy, confirmation of triazine-resistant biotypes, and common occurrence of seeds of these weed species on machinery. Low crop prices have also caused reduced pronamide use, contributing to increased occurrence of hair fescue. Results are guiding future research priorities for weed management in lowbush blueberry.
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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 | 0.001 |
| 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 | 0.000 |
| 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 it