MétaCan
Menu
Back to cohort
Record W2023459988 · doi:10.1016/j.yqres.2010.06.005

Autumn snowfall and hydroclimatic variability during the past millennium inferred from the varved sediments of meromictic Lake A, northern Ellesmere Island, Canada

2010· article· en· W2023459988 on OpenAlexaffabout
Jessica Tomkins, Scott F. Lamoureux, Dermot Antoniades, Warwick F. Vincent

Bibliographic record

VenueQuaternary Research · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalCenter for Northern StudiesQueen's University
Fundersnot available
KeywordsVarveMeltwaterSnowGeologyArcticPhysical geographySedimentologyClimatologyProxy (statistics)Period (music)SedimentDrainage basinClimate changeHoloceneOceanographyGeomorphologyGeography

Abstract

fetched live from OpenAlex

Abstract We examined the hydroclimatic signal in a record of annual lamina (varve) thickness from High Arctic Lake A, Ellesmere Island (83°00.00′N, 75°30.00′W). In this unglacierized catchment, nival melt is the dominant source for meltwater and transport of sediment to the lake, and autumn snowfall is highly influential on varve thickness through the amount of snow available for melt in the following year. For the period during which climatic data are available, varve thickness in Lake A was significantly correlated ( r = 0.50, p < 0.01) with the cumulative snowfall from August to October (ASO) during the previous year and to a lesser extent, ASO mean daily temperature ( r = 0.39, p < 0.01) at Alert, Nunavut (175 km east). The varve thickness record, interpreted as a proxy record of ASO snowfall and by extension, ASO temperature, indicated above-mean conditions during five periods of the past millennium, including most of the 20th century. These results corresponded well to other available high-resolution proxy climate records from the region, with some discrepancies prior to AD 1500 and during the period AD 1700–1900.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
GPT teacher head0.265
Teacher spread0.231 · 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 teacher head, not a consensus.

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

Citations12
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueQuaternary ResearchSame topicClimate change and permafrostFrench-language works237,207