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Record W4256661069 · doi:10.2307/1552463

Spatial and Temporal Changes in Sedimentary Processes at Proglacial Bear Lake, Devon Island, Nunavut, Canada

2002· article· en· W4256661069 on OpenAlexafffundabout
Ted G. Lewis, Robert G. Gilbert, Scott F. Lamoureux

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaArctic Institute of North America
KeywordsSedimentary rockGeologyPhysical geographyGeomorphologyOceanographyGeographyPaleontology

Abstract

fetched live from OpenAlex

AbstractLacustrine sedimentary processes are identified on an intra-annual scale at proglacial Bear Lake, Devon Island. Stage recorders, recording thermistors, sediment traps, and underflow monitoring equipment were deployed during the 1999 melt season. Episodic proximal turbidity currents were measured as positive near-bottom temperature anomalies and currents. The timing of individual positive temperature anomalies was clearly associated with diurnal peaks of discharge into the lake. During the period of 23 to 25 July, continuous underflow occurred, which preceded a large discharge event by about 24 h. Sediment traps placed throughout the lake recorded sediment accumulation rates from 16 June to 4 August. An extremely large precipitation event occurred on 29 June, when coarse, carbonate-rich sediment was deposited in front of a secondary tributary by spatially limited turbidity currents. A niveo-eolian deposit was observed on the lake ice, and sediment traps were deployed under this area. Mass accumulation rates in these traps locally overwhelmed fluvially generated sedimentation. This sediment was dominantly sand, which quickly melted through the lake ice and hastened the date of localized break-up.

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 score0.418
Threshold uncertainty score0.994

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.032
GPT teacher head0.259
Teacher spread0.227 · 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

Citations23
Published2002
Admission routes3
Has abstractyes

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