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Record W4328114227 · doi:10.1177/15501906231159033

Subfossil Insect Collections From the Arctic of Northeast Asia and Northwest North America

2023· article· en· W4328114227 on OpenAlexaboutno aff
Svetlana Kuzmina

Bibliographic record

VenueCollections A Journal for Museum and Archives Professionals · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersRussian Foundation for Basic Research
KeywordsSubfossilPleistoceneArcticCenozoicFaunaArchaeologyGeographyNeogeneQuaternaryGeologyEcologyPhysical geographyOceanographyHolocenePaleontologyStructural basinBiology

Abstract

fetched live from OpenAlex

Subfossil insects (mostly beetles) are common in the Late Cenozoic terrestrial loose deposits of the Arctic. The best-studied areas are those regions that remained ice-free during the Pleistocene — in Alaska and Yukon, northeast Siberia, and west Chukotka. Tertiary subfossil insects have been found in Alaska, Yukon, Canadian Northwest Territories, and Greenland. The northernmost sites (above 75°N) are Ellesmere and Meighen Islands in Canada, Kap Kobenhavn in Greenland, and Faddeyevsky and Navaya Sibir’ Islands in Siberia. Collections from North America and Russia are housed in research institutions such as The Geological Survey of Canada (GSC) in Ottawa, University of Alberta (UofA) in Edmonton, Paleontological Institute (PIN) in Moscow, and Institute of Plant and Animal Ecology in Yekaterinburg; collections from Greenland are housed in the Zoological Museum, University of Copenhagen. The collections are mainly used for research purposes, including reconstruction of past climate and environment, stratigraphy, origin of local faunas, and ecosystems of the past. A few extinct species have been described from the late Neogene and early Quaternary.

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 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 score0.017
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.273
Teacher spread0.244 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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