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Record W6946122588 · doi:10.25921/q8tw-sz93

NOAA/WDS Paleoclimatology - JR01 Lake Surface Temperature Reconstructions during the Holocene

2022· dataset· en· W6946122588 on OpenAlexaboutno aff

Bibliographic record

VenueNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsHoloceneDiatomPaleoclimatologyArcticPollenClimate changePeninsulaBefore Present

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.574
Threshold uncertainty score0.856

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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