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Record W2328449571 · doi:10.4039/tce.2014.8

Using ground beetles (Coleoptera: Carabidae) to control slugs (Gastropoda: Pulmonata) in salad greens in the laboratory and greenhouse

2014· article· en· W2328449571 on OpenAlexafffund
Justin M. Renkema, G. Christopher Cutler, D. Blanchard, Andrew M. Hammermeister

Bibliographic record

VenueThe Canadian Entomologist · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMollusks and Parasites Studies
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsGastropodaPulmonataBiologyCutwormPredationCropGreenhousePEST analysisAgronomyHorticultureEcology

Abstract

fetched live from OpenAlex

Abstract Ground beetles common in temperate agroecosystems are predators of crop pests, including slugs (Gastropoda: Pulmonata). Salad green production in greenhouses during autumn and spring can be limited by damage due to slugs and other pests. Introducing ground beetles to greenhouses may help reduce damage and improve yields. In the laboratory, while arenas with only slugs produced nearly no harvestable leaves, the presence of Carabus nemoralis Müller (Coleoptera: Carabidae) increased the number and weight of harvestable leaves to 55% of the amount in control arenas (without slugs or beetles), in addition to reducing the number of slugs. In a second experiment, adult or second-instar Pterostichus melanarius (Illiger) (Coleoptera: Carabidae) were released into greenhouse mesocosms (75 cm diameter steel rings) containing salad greens and slugs. Neither adults nor larvae improved the number or weight of harvestable leaves at the first two harvests, and there was no evidence of slug consumption. Towards the end of the experiment cutworms were common in the mesocosms and contributed to damaging salad greens. Adult P. melanarius likely consumed some cutworms, resulting in small increases in salad green yields at the third harvest. Our results suggest that ground beetles should be further examined as part of an integrated approach to pest control in late and early season salad green production in greenhouses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.241
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2014
Admission routes2
Has abstractyes

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