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Record W2317101316 · doi:10.1139/cjes-2012-0143

Holocene sediments from a coastal lake on northern Devon Island, Nunavut, Canada

2013· article· en· W2317101316 on OpenAlexaffvenueabout
Colin J. Courtney Mustaphi, Konrad Gajewski

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVarveGeologyHolocenePermafrostSedimentDeposition (geology)ArcticSedimentationOceanographyStructural basinPlateau (mathematics)StratigraphyPhysical geographyGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

Sediment cores from Lake DV09, northern Devon Island, Nunavut, Canada (75°34′34″N, 89°18′55″W), were studied to reconstruct the lake ontogeny through analysis and interpretation of the sediment stratigraphy. The lake was uplifted from marine inundation ∼7600 cal BP. After a millennium of rapid sediment accumulation, which coincided with the Holocene Thermal Maximum in the region, accumulation rates decreased over the past 6000 years as the Arctic became colder. This resulted in the deposition of very fine laminae that were interpreted as varves. The uppermost laminated sediments provided a ∼1600 year history of annual sediment transport and deposition into the lake. During periods of warmer temperatures, such as between 6000 and 7500 cal BP and during the Medieval Climate Anomaly (∼950–1300 CE; CE, Christian Era), hydroclimatic and permafrost slope processes increased sedimentation rates into the basin.

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 score0.017
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.001
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 head0.016
GPT teacher head0.207
Teacher spread0.191 · 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

Citations14
Published2013
Admission routes3
Has abstractyes

Explore more

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