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Record W2319621608 · doi:10.1061/40655(2002)61

Circulation and Exchange in a Small Subembayment of Lake Ontario

2002· article· en· W2319621608 on OpenAlexaboutno aff
Francisco J. Rueda, Edwin A. Cowen, Aaron R. Blake, K. L. Kull

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsStratification (seeds)ResidenceEnvironmental scienceResidence time (fluid dynamics)BayAquatic ecosystemEcosystemOceanographyHydrology (agriculture)EcologyEngineeringGeology

Abstract

fetched live from OpenAlex

Mathematical models of aquatic ecosystems in well-mixed laboratory flow reactors suggest that hydraulic residence time is a key variable in determining the extent that ecosystems are self-organized or dominated by outside influence. Our study is part of a larger project "Lake Ontario Biocomplexity Study: Physical, biological, and Human interactions shaping the ecosystems of freshwater bays and lagoons", funded through the National Science Foundation (NSF) Biocomplexity in the Environment Program, which attempts to extend this theory into natural freshwater embayments. The physical environment in these embayments is more complicated than laboratory well-mixed reactors, and therefore their residence times are not easily identifiable. The embayments are subject to stratification, tidal and wind forcing, forming a balance of competing forces the former effectively prevents vertical mixing while the latter two drive the system toward a vertically, and horizontally well mixed state. Processes influencing their mean residence time are reviewed in this work and investigated in the particular case of Little Sodus Bay (LSB), a small freshwater subembayment of Lake Ontario.

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)0.000
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 score0.105
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.030
GPT teacher head0.194
Teacher spread0.164 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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