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Record W1992455340 · doi:10.1191/0959683604hl717rp

A diatom-based Holocene palaeoenvironmental record from a mid-arctic lake on Boothia Peninsula, Nunavut, Canada

2004· article· en· W1992455340 on OpenAlexaffabout
Michelle G. LeBlanc, Konrad Gajewski, Paul B. Hamilton

Bibliographic record

VenueThe Holocene · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsCanadian Museum of NatureUniversity of Ottawa
Fundersnot available
KeywordsDiatomHoloceneArcticMacrofossilRadiocarbon datingOceanographyGeologyEcologyFlora (microbiology)PeninsulaClimate changePalynologyPhysical geographyPollenGeographyPaleontologyBiology

Abstract

fetched live from OpenAlex

A 485 cm sediment core from a lake unoffcially called JR01, Boothia Peninsula, Nunavut, Canada, yielded a high-resolution diatom record documenting environmental change in the mid-arctic. Five radiocarbon dates provided the chronology. Changes in diatom composition and sediment character istics indicated distinct shifts in the Holocene climate. A more diverse and productive diatom flora implies warmer temperatures in the middle Holocene. A subsequent complete shift in diatom composition to a predominantly Fragilaria sensu lato flora and a reduction in diversity and production suggests cooler climates in this region after 4600cal. BP. Smaller-scale climatic fluctuations, such as the‘Little Ice Age’ (LIA, 600–150 cal. BP) and the‘Mediaeval Warm Period’ (MWP, 1150–600 cal. BP), caused shifts in the diatom flora and production. Subtle shifts in floristic diversity within the LIA may reflect climatic variability at a century scale. A gradual shift to a more diverse and productive flora in the last 150 years suggests a response to the recent warming trend.

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)0.000
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 score0.014
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.013
GPT teacher head0.204
Teacher spread0.192 · 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

Citations47
Published2004
Admission routes2
Has abstractyes

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