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Climate‐related variations in mixing dynamics in an Alaskan arctic lake

2009· article· en· W2037315250 on OpenAlexaff
Sally MacIntyre, Jonathan P. Fram, Paul J. Kushner, Neil D. Bettez, W. John O’Brien, John E. Hobbie, George W. Kling

Bibliographic record

VenueLimnology and Oceanography · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Toronto
FundersDivision of Environmental BiologyDivision of Ocean SciencesOffice of Polar ProgramsNational Science Foundation
KeywordsWater columnAtmospheric sciencesEnvironmental scienceEddy diffusionBuoyancyArcticClimatologyMixed layerFlux (metallurgy)Diel vertical migrationMean radiant temperatureWind speedThermoclineGeologyClimate changeOceanographyMeteorologyTurbulenceChemistryThermodynamics

Abstract

fetched live from OpenAlex

Mean epilimnetic temperatures from mid‐June through mid‐August in a small, arctic kettle lake had no trend from 1975 to 2008 and varied annually up to ±3°C relative to the mean. Analysis of data from temperature arrays deployed on the lake from 1998 to 2007 showed that as mean summer temperatures shifted from 2.5°C below the mean, to the mean, and to 3°C above the mean, deepening of the mixed layer during cold fronts decreased, average metalimnetic thickness increased from 2 to 5 m, maximum values of water‐column stability increased fourfold, minimum values of Lake numbers (L N ) increased from ≤1 to 10, the metalimnetic coefficient of eddy diffusivity (K z ) decreased from 10 −5 m 2 s −1 to 10 −7 m 2 s −1 , and time scales for mixing across the metalimnion increased from days to months. Mean surface temperatures and mixing regimes were significantly correlated with mean air temperatures, but not with mean insolation, or mean wind speeds during summer. They also depended upon the frequency and persistence of events with higher winds, heating, or cooling. In summers with cold surface temperatures, the surface energy fluxes that induce mixing by heat loss were low but with frequent wind events heat was mixed downwards, leading to lower stability. The warmest surface temperatures resulted when atmospheric conditions led to persistent positive buoyancy flux in early summer and winds were elevated primarily on diel cycles as opposed to longer ones. Summers with cooler water temperatures and enhanced vertical mixing are linked to frontal activity and low atmospheric pressure near the northern Alaskan coast.

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 score0.104
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.229
Teacher spread0.221 · 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

Citations137
Published2009
Admission routes1
Has abstractyes

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