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Record W4390038556 · doi:10.1080/07055900.2023.2287454

A Circulation Model for Baynes Sound, British Columbia, Canada

2023· article· en· W4390038556 on OpenAlexafffundvenueabout
Maxim V. Krassovski, Michael Foreman, Thomas Guyondet, Ramón Filgueira, Terri F. Sutherland

Bibliographic record

VenueATMOSPHERE-OCEAN · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsDalhousie UniversityFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsEnvironmental scienceContext (archaeology)Sound (geography)Biogeochemical cycleClimatologyEstuaryOceanographyRange (aeronautics)Sea surface temperatureProxy (statistics)Atmospheric sciencesMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

An application of the three-dimensional Finite Volume Community Ocean Model was developed for Baynes Sound, British Columbia, Canada and a simulation was carried for the period of May 2016 to April 2017. The objective was to provide highly resolved spatial estimates of the regional hydrodynamics that could be used in a coupled biogeochemical model to assess the cultured shellfish carrying capacity of the region. The issue of how representative the particular simulation period was in the context of typical seasonal features was addressed by comparing temperatures, salinities and river discharges with long-term statistics. Circulation model results were generally in good agreement with salinity, temperature, velocity, and sea surface height observations, providing confidence in subsequent seasonal estimates of volume fluxes through the two entrances and water-renewal. Approximately sixty percent of the variability in near-surface temperature observations at five locations was shown to be linearly dependent on a combination of the along-sound wind, air temperature, and the daily sea surface range, which was taken as a proxy for tidal mixing.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.193
Teacher spread0.181 · 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

Citations0
Published2023
Admission routes4
Has abstractyes

Explore more

Same venueATMOSPHERE-OCEANSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207