MétaCan
Menu
← Back to cohort
Record W2329634103 · doi:10.1061/40990(324)3

A Five-Level Nested-Grid Coastal Ocean Circulation Prediction System for Canadian Atlantic Coastal Waters

2008· article· en· W2329634103 on OpenAlexaffabout
Jinyu Sheng, Bo Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHydrographyDownwellingOceanographyClimatologyForcing (mathematics)UpwellingEnvironmental scienceOcean currentSea surface temperatureStormMeteorologyGeologyGeography

Abstract

fetched live from OpenAlex

A five-level nested-grid coastal ocean circulation prediction system known as the NCOPS-LB was developed for simulating the three-dimensional (3D) circulations and hydrographic distributions over Canadian Atlantic coastal waters. The system is the integration of a shelf circulation forecast system known as Dalcoast 3 and a high-resolution coastal circulation model for Lunenburg Bay (LB). The NCOPS-LB is driven by meteorological forcing based on 3-hourly weather forecast fields provided by the Meteorological Service of Canada and astronomical forcing produced by WebTide developed by the Department of Fisheries and Oceans of Canada. The prediction system is used to study the dynamic response of the inner Scotian Shelf to tropical storm Alberto in June 2006. A comparison of model results with the observations made in LB demonstrates that the NCOPS-LB has reasonable skills in predicting surface elevations, currents, and hydrographic distributions associated with coastal upwelling/downwelling over the inner Scotian Shelf.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.017
GPT teacher head0.178
Teacher spread0.160 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2008
Admission routes2
Has abstractyes

Explore more

Same topicOceanographic and Atmospheric Processes→French-language works237,207→