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Record W2102722859 · doi:10.2166/wqrjc.2012.001

Frequency of episodic stratification in the near surface of Lake Opeongo and other small lakes

2012· article· en· W2102722859 on OpenAlexafffundabout
Patricia Pernica, Mathew G. Wells

Bibliographic record

VenueWater Quality Research Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Research and InnovationUniversity of TorontoMinistry of Natural Resources
KeywordsEpilimnionStratification (seeds)ThermoclineEnvironmental scienceHypolimnionDaytimeAtmospheric sciencesClimatologyUpwellingOceanographyGeologyChemistryEutrophication

Abstract

fetched live from OpenAlex

Wind-driven mixing in the epilimnion of a deep lake can be suppressed when there is a weak near surface stratification, which occurs frequently during periods of strong solar heating and weak winds. Using data from a vertical chain of fast response thermistors, we analyze the frequency of near surface stratification in the top 2 meters of the epilimnion in Lake Opeongo, Ontario for the periods between May and August in 2009 and 2010. Near surface thermoclines (as defined by dT/dz > 0.2 °C m−1 between 1 and 2 m) occur for 24% of the sampling period in 2009, 37% of the sampling period in 2010 and correspond to periods of high values of gradient Richardson number. During daytime the epilimnion is stratified up to 45% of the time. At night, cooling generally leads to a more isothermal profile, but near surface thermoclines still form at least 20% of the time. Extended periods of near surface stratification (>1 h), account for more than 80% of the stratified period. We compare these findings with previous observations from the Experimental Lakes Area in Northern Ontario, and discuss the biological implications of episodic stratification.

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)0.001
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 score0.471
Threshold uncertainty score0.937

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.115
GPT teacher head0.360
Teacher spread0.244 · 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

Citations10
Published2012
Admission routes3
Has abstractyes

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