Weed seed granivory by carabid beetles and crickets for biological control of weeds in commercial lowbush blueberry fields
Bibliographic record
Abstract
Abstract Weeds are one of the most limiting factors in the commercial production of lowbush blueberries V accinium angustifolium ( E ricaceae). Sheep sorrel ( R umex acetosella ) and hairy fescue ( F estuca tenuifolia ) are prominent weeds in lowbush blueberry fields. Because the granivorous insects H arpalus rufipes ( C arabidae) and G ryllus pennsylvanicus ( G ryllidae) are common in lowbush blueberry fields when sheep sorrel and hairy fescue are dispersing seeds, we examined how granivorous insects can contribute to the biocontrol of these weeds. In the laboratory, H . rufipes and G . pennsylvanicus consumed a significant number of seeds of sheep sorrel and hairy fescue, and a field experiment found that insects probably consume a significant number of sheep sorrel and hairy fescue seeds in blueberry fields. Additional experiments found that H . rufipes was highly susceptible to field rates of phosmet and acetamiprid, although not to field rates of spirotetramat, which are insecticides that may be used in blueberry fields when the beetle is active. Natural populations of granivorous insects probably provide a valuable ecological service in commercial lowbush blueberry fields and should be conserved in the development of integrated weed management programmes.
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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) | 0.001 | 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".