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Record W1971514885 · doi:10.1029/2012jc008151

Modeling seasonal to interannual ocean variability of coastal British Columbia

2012· article· en· W1971514885 on OpenAlexaffabout
Diane Masson, I. Fine

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsHindcastForcing (mathematics)ClimatologyWind stressOceanographyCurrent (fluid)Environmental scienceGeologyOcean general circulation modelOcean currentBoundary currentGeneral Circulation ModelClimate change

Abstract

fetched live from OpenAlex

A regional circulation model is used to examine the ocean variability along the Pacific coast of Canada. The model domain extends from the Columbia River to the Alaska Panhandle and is applied to hindcast the period 1995–2008. An extensive model validation indicates that the modeled ocean varies realistically at the seasonal to interannual time scale, including the marked regional response to the 1997–1998 El Niño. The model is used to better understand the ocean response to direct forcing of wind and rivers over the model domain relative to variability outside of the study area. To do so, additional runs are conducted with and without river discharge and seasonal variability in wind stress as well as with and without interannual variability in lateral boundary conditions. It is shown that the seasonal variability of the coastal circulation, such as the reversal of the shelf‐break current and summer intensification of the California undercurrent, is of mixed origin, with both remote and local forcing contributing importantly. On the other hand, the modeled strong oceanic anomalies associated with the 1997–1998 El Niño originate from anomalous conditions on the lateral boundaries, except for the locally forced surface warm anomalies.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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

Citations50
Published2012
Admission routes2
Has abstractyes

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