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Record W2130734608 · doi:10.4039/tce.2012.6

Muscidae (Diptera) diversity in Churchill, Canada, between two time periods: evidence for limited changes since the Canadian Northern Insect Survey

2012· article· en· W2130734608 on OpenAlexaffabout
Anais Krystel Renaud, Robert E. Roughley

Bibliographic record

VenueThe Canadian Entomologist · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsBishop's UniversityUniversity of Manitoba
Fundersnot available
KeywordsNettingSpecies richnessMuscidaeEcologyAssemblage (archaeology)MalaiseBiodiversityBiologyGeography

Abstract

fetched live from OpenAlex

Abstract A survey of muscid flies from Churchill, Manitoba, Canada, in 2007 yielded 155 species. Some components of species richness and composition of this contemporary assemblage were compared with those of a historical (pre-1965) assemblage, and the contribution of the three collecting methods used in the 2007 survey protocol was evaluated. Estimates of species richness indicated that Malaise traps yielded more species than did pan traps or sweep netting, and that species composition did not differ significantly between Malaise trap and pan trap catches. These results suggest that Malaise traps and sweep netting are adequate methods to survey northern Muscidae. We report little difference in estimated species richness and composition between time periods for material collected by sweep netting. When all material from the 2007 survey was pooled, 87% of the pre-1965 species were collected again in 2007. Most nonoverlapping species between time periods were rare in samples and (or) collected by different methods, suggesting a failure to detect as the most likely explanation for their absence in one assemblage. Nevertheless, the proportion of aquatic and semiaquatic species of Spilogona Schnabl was more than twice as high in the list of species not recovered in 2007 than in the pre-1965 assemblage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.132
GPT teacher head0.270
Teacher spread0.139 · 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 teacher head, not a consensus.

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

Citations11
Published2012
Admission routes2
Has abstractyes

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