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Record W3126729524 · doi:10.1080/20442041.2020.1843930

Intense variability of dissolved oxygen and temperature in the internal swash zone of Hamilton Harbour, Lake Ontario

2021· article· en· W3126729524 on OpenAlexafffundabout
Bryan Flood, Mathew G. Wells, Jonathan D. Midwood, Jill L. Brooks, Yulong Kuai, Jingzhi Li

Bibliographic record

VenueInland Waters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsThe Scarborough HospitalCarleton UniversityFisheries and Oceans CanadaUniversity of Toronto
FundersEnvironment and Climate Change CanadaGreat Lakes Protection Fund
KeywordsSwashHarbourHydrology (agriculture)Environmental scienceOxygenOceanographyGeologyGeomorphologyChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

The thermocline of large, stratified lakes is constantly sloshing along the sloping bed, creating a spatially variable internal swash zone. Temperature and dissolved oxygen vary rapidly here, potentially impacting fish habitat on timescales of hours. Large spatial differences in the time-dependent variance of temperature around Hamilton Harbour, Lake Ontario, Canada, were partly controlled by basin shape and bathymetry. The temporal variability was nearly twice as large at sites along the mildly sloping, narrow, upwind end of the basin relative to those at a similar depth at the steeper, broad, downwind end. Because the thermocline and oxycline were coincident, the same physical mechanisms resulted in a dissolved oxygen variance also twice as great at the mild slope compared to the steeper slope. Frequent hypoxic events occurred throughout the internal swash zone, drastically reducing the availability of fish habitat for anoxia-intolerant species. In the dynamic littoral zone, weekly measurements would overlook the acute temporal variability of temperature and dissolved oxygen. Here, we demonstrate that field observations and 3-dimensional (3D) hydrodynamic modelling can predict how basin morphometry affects internal seiche dynamics and spatial variability of internal swash zones.

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)0.000
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 score0.021
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations34
Published2021
Admission routes3
Has abstractyes

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