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Record W2902417862 · doi:10.2166/wqrj.2018.011

Mapping the summer 2017 surface water circulation of Picton Bay, ON

2018· article· en· W2902417862 on OpenAlexafffund
Jennifer A. Shore, Peter Snell

Bibliographic record

VenueWater Quality Research Journal · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsRoyal Military College of Canada
FundersEnvironment and Climate Change Canada
KeywordsBayDrifterEnvironmental scienceCurrent (fluid)Forcing (mathematics)OceanographySurface waterOcean currentLagrangianHydrology (agriculture)MeteorologyGeologyClimatologyGeographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract On March 24, 2017, a fuel spill from a partially submerged barge in Picton Bay contaminated the source water for the drinking water supply of the local township. Immediately after the spill, management decisions regarding the water intake plant operations were made based on contaminant observations and projected wind conditions. From a management perspective, it is essential to understand all the dynamical forcing for a system to direct the best decision-making but, unfortunately, there are no historical observations of currents in Picton Bay or any in-depth numerical modelling studies that have established the circulation patterns or hydrodynamics of the bay. This paper presents observations of surface speeds and drifter pathways collected using Lagrangian drifters and compares the observations to the velocity field estimates from a wind forced three-dimensional hydrodynamic model. Surface drifters were deployed from July to September and moved southwest into the bay during each deployment with almost no tendency to turn and drift out of the bay. Model simulations indicated that currents in the bay are sensitive to small-scale local winds and that a boundary current exists that connects the spill site to the area of the water intake pipes in wind conditions that are to the southwest or southeast.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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)0.0030.001

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 head0.215
GPT teacher head0.370
Teacher spread0.156 · 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.

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

Citations2
Published2018
Admission routes2
Has abstractyes

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