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Record W4285008400 · doi:10.3897/zookeys.1111.72345

Checklist of the caddisflies (Insecta, Trichoptera) of the Upper Midwest region of the United States

2022· article· en· W4285008400 on OpenAlexaboutno aff
David C. Houghton, R. Edward DeWalt, T.S. Hubbard, Kurt L. Schmude, Jeffrey J. Dimick, Ralph W. Holzenthal, Roger J. Blahnik, J. L Snitgen

Bibliographic record

VenueZooKeys · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
FundersHuron Mountain Wildlife FoundationIndiana Department of Natural ResourcesMichigan Department of Natural ResourcesWisconsin Department of Natural ResourcesMinnesota Department of Natural ResourcesIllinois Department of Natural ResourcesU.S. Environmental Protection AgencyNational Science Foundation
KeywordsSpecies richnessFaunaGeographyCaddisflyEcologyChecklistArchaeologyBiologyLarva

Abstract

fetched live from OpenAlex

Five hundred and fifty-two caddisfly species are reported from the Upper Midwest region of the United States, an area that includes 13 states and ~ 2 million km 2 . Of these, 62 species are reported for the first time from the state of Iowa, 25 from Wisconsin, 18 from South Dakota, 12 from Illinois, five from Indiana, four from North Dakota, four from Minnesota, and one from Nebraska. The Upper Midwest fauna contains nearly 40% of all species known from the United States and Canada, as well as 22 species endemic to the region. Overall species richness was highest in Michigan (319 species), Kentucky (296), Minnesota (292), and Wisconsin (284). Differences in state species assemblages within the region largely followed a geographic pattern, with species richness declining in the western prairie states. There are almost certainly further species remaining to be found in this large region.

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)0.000
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 score0.822
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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