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Record W4384912529 · doi:10.4039/tce.2023.11

The effect of season and urbanisation on Calliphoridae (Diptera) diversity in British Columbia, Canada, using baited traps

2023· article· en· W4384912529 on OpenAlexafffundabout
Cassidy A.R. Smith, Lisa M. Poirier, Gail S. Anderson

Bibliographic record

VenueThe Canadian Entomologist · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsUniversity of Northern British ColumbiaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsCalliphoridaeHabitatAbundance (ecology)EcologyForensic entomologyBiologyUrbanizationGeography

Abstract

fetched live from OpenAlex

Abstract Forensic entomology is an important component of criminal investigations, providing information surrounding a death using region-specific data on the local necrophagous community. To understand the community within the Metro Vancouver region of British Columbia, Canada, a field study monitored the abundance and diversity of necrophagous Calliphoridae (Diptera) over a nine-month period in distinct terrestrial environments. Baited bottle traps ( n = 9) were deployed weekly for 12-hour intervals in three different environments. Species, sex, and gravidity of collected specimens were determined. Bivariate analyses revealed significant relationships between species, geographic location, and month of collection, suggesting that Calliphoridae species composition is influenced by habitat type and seasonal shifts in temperature. Sex ratios and reproductive ranges of Calliphoridae differed among the habitats sampled.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.209
Teacher spread0.189 · 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
Published2023
Admission routes3
Has abstractyes

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