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Record W2746231610 · doi:10.22543/0090-0222.1052

The Northern Widow Spider, Latrodectus Variolus (Araneae: Theridiiae), in Michigan

2017· article· en· W2746231610 on OpenAlexaboutno aff
Louis F. Wilson

Bibliographic record

VenueThe Great Lakes Entomologist · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsSpiderBiologyWolf spiderZoologyGenusEcology

Abstract

fetched live from OpenAlex

Until recently the species of widow spider occurring in Michigan was considered a variant of the black widow, Latrodectus mactans (Fabricius). Levi (1959) summarized and revised the worldwide genus Latrodectus, placing the southern areas of Illinois, Indiana, and Iowa as the northern limit of mactans on the North American continent. Widow spiders located north of this became part of the widespread curacaviensis group. After further information was available, McCrone and Levi (1964) revised the curacaviensis group, establishing the species that extended north of mactans as the northern widow, L. variolus, originally described by Walckenaer (183v). L. variolus is the most northern representative of the genus in North America, but its range extends from southern Canada and the northern States south to northern Florida west through Texas to central California. It is sympatric with L. mactans in many southern states , and it is sympatric with both L. mactans and L. bishopi in Florida (McCrone and Levi, 1964). The northern widow is known from many localities in Michigan, but in recent years it has been abundant in the northwestern part of the Lower Peninsula--particularly in Kalkaska, Grand Traverse, and Wexford counties. McCrone and Levi (1964) recorded specimens from Calhoun, Cheboygan, and Barry counties. In addition, specimens have been collected or reported from Antrim, Charlevoix, Crawford, Emmet, Livingston, Otsego, and Wayne counties. Observations were made in several localities in 1965 and 1966 in order to learn more about this spider's coloration, abundance, and habits.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.824
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.020
GPT teacher head0.254
Teacher spread0.235 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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