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Record W3117580951 · doi:10.1017/s000748532000070x

Changes in the recovery of insects in pitfall traps associated with the age of cow dung bait fresh or frozen at the time of placement

2020· article· en· W3117580951 on OpenAlexaffabout
G.A. Bezanson, C.D. Dovell, Kevin D. Floate

Bibliographic record

VenueBulletin of Entomological Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Lethbridge
Fundersnot available
KeywordsBiologyPitfall trapToxicologyEcologyAnimal scienceAbundance (ecology)

Abstract

fetched live from OpenAlex

Pitfall traps baited with cattle dung are commonly used to characterize local assemblages of coprophilous insects. Baits can be made fresh or be prepared in advance and kept frozen until needed. Insect recoveries are expected to decline with the age of the bait and may be affected by the use of fresh vs. frozen baits. To assess the effect of these two factors on insect recoveries, we performed a pitfall trap experiment that was repeated in four trials spanning 2 years and two locations in southern Alberta, Canada. The experimental design allowed us to minimize the potential confounding effect of short-term weather events. For results combined across trials, baits aged >3 days were largely ineffective for attracting coprophilous species. Frozen baits attracted significantly more insects than did fresh dung for the first 3 days after placement in the field with no difference thereafter. Our findings suggest that insect recoveries in dung-baited pitfall traps can be maximized with the use of frozen baits with replacement every 3-4 days.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.158
GPT teacher head0.287
Teacher spread0.129 · 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 designObservational
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

Citations8
Published2020
Admission routes2
Has abstractyes

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