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Record W2319953428 · doi:10.1002/2015jc011085

Water renewals in the<scp>S</scp>aguenay<scp>F</scp>jord

2015· article· en· W2319953428 on OpenAlexafffundabout
Mélany Belzile, Peter S. Galbraith, Daniel Bourgault

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsCégep de RimouskiFisheries and Oceans CanadaUniversité du Québec à Rimouski
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsFjordSillHydrographyMooringGeologyOceanographyThermoclineStructural basinWater columnTemperature salinity diagramsWater massTransectSeasonalitySalinityGeomorphologyBiology

Abstract

fetched live from OpenAlex

Abstract Water renewals and renewal times of the Saguenay Fjord are investigated and classified according to their intrusion depth. Renewal dynamics are controlled by a shallow sill (∼20 m) at the fjord mouth, by large tides that are a distinguishing feature of the Saguenay Fjord and by large vertical mixing inside the inner basin ( ). A mooring was deployed in the inner basin of the fjord to provide a clearer quantitative understanding of the complexity and seasonality of water renewals in this seasonally ice‐covered fjord. The mooring provided information on currents over nearly the entire water column, along with temperature‐salinity at a few discrete depths. Hydrographic temperature and salinity transects spanning multiple seasons and years as well as turbulence profiles were also collected. The observations show that the fjord dynamics are more complex than previously hypothesized, with large changes in renewal event depths leading to three different renewal regimes. Part of this renewal depth variability may be explained by the seasonality of the St. Lawrence estuarine circulation. Because of the large turbulence within the inner basin bottom layer, the density decreases over time such that new deep renewals can occur every year. The mechanisms behind the large vertical mixing cannot yet be clearly identified but a statistically significant correlation ( ) suggests that internal wave breaking may be a significant contributor to deep turbulence mixing in the inner basin. The renewal time of the inner basin waters is estimated to be between 1 and 6 months.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
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)0.0000.000

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 head0.054
GPT teacher head0.302
Teacher spread0.248 · 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 designNot applicable
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

Citations26
Published2015
Admission routes3
Has abstractyes

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