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Record W2775080550 · doi:10.1002/2017gl075373

High‐Frequency Observations of Temperature and Dissolved Oxygen Reveal Under‐Ice Convection in a Large Lake

2017· article· en· W2775080550 on OpenAlexafffund
Bernard Yang, Joelle D. Young, Laura C. Brown, Mathew G. Wells

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMinistry of the Environment, Conservation and ParksThe Scarborough HospitalGeneral Electric (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvectionAtmospheric sciencesOxygenSupersaturationGeologyClimatologyConvective mixingEnvironmental scienceMeteorologyChemistryGeography

Abstract

fetched live from OpenAlex

Abstract Detailed observations of thermal structure over an entire winter in a large lake reveal the presence of large (10–20 m) overturns under the ice, driven by diurnal solar heating. Convection can occur in the early winter, but the most vigorous convection occurred near the end of winter. Both periods are when our lake ice model suggest thinner ice that would have been transparent. This under‐ice convection led to a deepening of the mixed layer over time, consistent with previous short‐term studies. During periods of vigorous convection under the ice at the end of winter, the dissolved oxygen had become supersaturated from the surface to 23 m below the surface, suggesting abundant algal growth. Analysis of our high‐frequency observations over the entire winter of 2015 using the Thorpe‐scale method quantified the scale of mixing. Furthermore, it revealed that changes in oxygen concentrations are closely related to the intensity of mixing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.996

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.283
Teacher spread0.249 · 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.

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
Published2017
Admission routes2
Has abstractyes

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