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Record W2018105444 · doi:10.1016/j.yqres.2007.04.003

Post-Glacial climatic change on Boothia Peninsula, Nunavut, Canada

2007· article· en· W2018105444 on OpenAlexaffabout
Susan Zabenskie, Konrad Gajewski

Bibliographic record

VenueQuaternary Research · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPollenTundraHoloceneCyperaceaePeninsulaGeologyArcticPhysical geographyVegetation (pathology)Glacial periodHolocene climatic optimumPalynologyClimate changeOceanographyPaleontologyEcologyGeographyArchaeologyPoaceaeBiology

Abstract

fetched live from OpenAlex

Abstract A high temporal resolution pollen diagram from a lake in the mid-Arctic region of the Boothia Peninsula, Nunavut, Canada, documents the history of the regional vegetation and climate for the past 7200 yr. Major tundra pollen taxa in the core include Cyperaceae and Salix, with Cyperaceae comprising over 50% of the pollen in the early and late Holocene. Tree pollen, transported from far to the south, comprised a large percentage of the pollen sum, with Pinus accounting for 30% of the pollen in some levels of the core. Pollen percentages and concentrations of taxa typical of the mid-Arctic were highest in the mid-Holocene, corresponding to warm conditions. Decreasing pollen concentrations indicate cooling temperatures, with more rapid decreases occurring around 4200, 3800–3400, and 2500 cal yr BP. Pollen percentages of Salix, Cyperaceae, and Artemisia increased in the past 35 yr in response to global warming. Reconstructions of July temperature using the modern analog technique showed that the mid-Holocene (5800–2800 cal yr BP) was approximately 1 °C higher than during the past 1000 yr.

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 score0.019
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.089
GPT teacher head0.336
Teacher spread0.246 · 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

Citations71
Published2007
Admission routes2
Has abstractyes

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