Weed seed granivory by <i>Gryllus pennsylvanicus</i> and its population dynamics in lowbush blueberry fields
Bibliographic record
Abstract
Granivory can significantly contribute to weed seed bank depletion in agroecosystems, but its role in weed biocontrol within commercial lowbush blueberry ( Vaccinium angustifolium, Ericaceae) remains understudied. This research investigated the spatiotemporal activity and seed-feeding tendencies of Gryllus pennsylvanicus (Gryllidae), a common granivore in lowbush blueberry fields, to evaluate its weed biocontrol potential. Field and laboratory studies demonstrated that G. pennsylvanicus remained active for 14 weeks, with peak activity occurring in mid-August, aligning with the seed rain of economically significant weed species. Pitfall trap captures of G. pennsylvanicus showed no variation with distance from the field edge, indicating consistent activity across field interiors and margins. Laboratory trials showed that G. pennsylvanicus consumed seeds from nine weed species, with seed masses ranging from 0.057 to 1.9 mg. In additional laboratory experiments with hair fescue ( Festuca filiformis)—a pervasive grass that reduces yields and obstructs harvest in many lowbush blueberry operations—individual crickets consumed an average of 65.5 seeds per day. These findings suggest that G. pennsylvanicus may play a substantial role in reducing weed seed banks and contribute meaningfully to integrated weed management in commercial lowbush blueberry production systems.
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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".