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Record W2172211126 · doi:10.1080/02724634.2014.880446

A sand tiger shark–dominated fauna from the Eocene Arctic greenhouse

2014· article· en· W2172211126 on OpenAlexafffundabout
Aspen Padilla, Jaelyn J. Eberle, Michael D. Gottfried, A R Sweet, J. Howard Hutchison

Bibliographic record

VenueJournal of Vertebrate Paleontology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNatural Resources CanadaNational Science Foundation
KeywordsCarchariasFaunaGeologyPaleontologyPalynologyArcticOceanographyEcologyBiologyJuvenile

Abstract

fetched live from OpenAlex

We describe a new shark fauna from Canada's westernmost Arctic island, Banks Island, Northwest Territories, based upon thousands of shark teeth recovered from lower–middle Eocene sediments of the Cyclic Member (Eureka Sound Formation) on northern Banks Island, Northwest Territories (∼74°N latitude). Based upon palynology, the sediments preserving the shark teeth are late early to middle Eocene in age and likely span the Early Eocene Climatic Optimum (EECO). The low-diversity faunal assemblage is dominated by the sand tiger sharks Striatolamia and Carcharias, but also includes relatively rare teeth of the carcharhinid Physogaleus (extinct relative of sharpnose and tiger sharks) and very rare teeth of the odontaspidid Odontaspis winkleri. We also report the occurrence of the ray Myliobatis. Based upon analogy with extant Carcharias taurus and Myliobatis, the presence of Carcharias and Myliobatis on northern Banks Island corroborates the relatively warm sea surface temperatures estimated by others for the Eocene Arctic Ocean. We hypothesize that the low diversity of the Banks Island shark fauna, in comparison with other early Eocene brackish, shallow-marine chondrichthyan assemblages such as the Abbey Wood (U.K.) fauna, is probably due to environmental factors, including reduced salinity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.214
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations15
Published2014
Admission routes3
Has abstractyes

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