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Record W2971181671 · doi:10.1080/15230430.2019.1640527

Environmental changes of the last 1000 years on Prince of Wales Island, Nunavut, Canada

2019· article· en· W2971181671 on OpenAlexaffabout
Camille Tamo, Konrad Gajewski

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCyperaceaePollenArcticPaleoclimatologyPhysical geographyPalynologyArchipelagoVegetation (pathology)Climate changeGeographyChronologyPoaceaeClimatologyGeologyOceanographyArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

A pollen record from a lake sediment core from southeastern Prince of Wales Island, Nunavut, Canada (SW08; 72.3177, −97.2678, 104 m a.s.l) provides the first high-resolution July temperature reconstruction for the last 1,000 years for the central Canadian Arctic Archipelago. The vegetation underwent marked transitions during the Little Ice Age (LIA; 1500–1800 CE) and Medieval Climate Anomaly (MCA; 1090–1250 CE), which was primarily observed in the proportion of Cyperaceae, Poaceae, and Salix pollen. Cyperaceae pollen was highest in the samples corresponding to the MCA, whereas Poaceae increased during the LIA. In the last 30 years, Salix and Betula pollen increased. The mean July temperature reconstruction showed a long-term cooling from 1080–1915 CE with a sustained cold period from 1800–1915 CE prior to twentieth-century warming. A synthesis of paleoclimate records from across the Arctic demonstrates that pollen-based reconstructions record both high and low frequency climate variability, when sampling resolution is sufficient, and can improve regional climate reconstructions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.238
Teacher spread0.222 · 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 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

Citations2
Published2019
Admission routes2
Has abstractyes

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