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Record W2311609547 · doi:10.1016/j.yqres.2016.02.003

Multiproxy paleoecological evidence of Holocene climatic changes on the Boothia Peninsula, Canadian Arctic

2016· article· en· W2311609547 on OpenAlexafffundabout
Marie-Claude Fortin, Konrad Gajewski

Bibliographic record

VenueQuaternary Research · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric Sciences
KeywordsHoloceneArcticPeninsulaDiatomGeologyPollenClimate changePhysical geographyOceanographyIce coreHolocene climatic optimumEcologyGeographyBiology

Abstract

fetched live from OpenAlex

A study of chironomid remains in the sediments of Lake JR01 on the Boothia Peninsula in the Central Canadian Arctic provides a high-resolution record of mean July air temperatures for the last 6.9 ka. Diatom and pollen studies have previously been published from this core. Peak Holocene temperatures occurred prior to 5.0 ka, a time when overall aquatic and terrestrial biological production was high. Chironomid-inferred summer air temperatures reached up to 7.5°C during this period. The region of Lake JR01 cooled over the mid- to late-Holocene, with high biological production between 6.1 and 5.4 ka. Biological production decreased again at ∼2 ka and the rate of cooling increased in the past 2 ka, with coolest temperatures occurring between 0.46 and 0.36 ka, coinciding with the Little Ice Age. Although biological production increased in the last 150 yr, the reconstructed temperatures do not indicate a warming during this time. During transitions, either warming or cooling, chironomid production increases, suggesting an ecosystem-level response to climate variability, seen at a number of lakes across the Arctic.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.133
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.181
GPT teacher head0.352
Teacher spread0.172 · 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

Citations6
Published2016
Admission routes3
Has abstractyes

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