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Record W3207575050 · doi:10.3389/fmars.2021.732269

The Gulf of St. Lawrence Biogeochemical Model: A Modelling Tool for Fisheries and Ocean Management

2021· article· en· W3207575050 on OpenAlexafffund
Diane Lavoie, Nicolas Lambert, Joël Chassé, Olivier G. J. Riche, Yvonnick Le Clainche, Kumiko Azetsu‐Scott, Béchir Béjaoui, James R. Christian, Denis Gilbert

Bibliographic record

VenueFrontiers in Marine Science · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsBiogeochemical cycleEnvironmental scienceOceanographyBiogeochemistryEstuaryNitrateCarbonateCalcium carbonatePhytoplanktonNutrientEnvironmental chemistryChemistryEcologyGeologyBiology

Abstract

fetched live from OpenAlex

The goal of this paper is to give a detailed description of the coupled physical-biogeochemical model of the Gulf of St. Lawrence that includes dissolved oxygen and carbonate system components, as well as a detailed analysis of the riverine contribution for different nitrogen and carbonate system components. A particular attention was paid to the representation of the microbial loop in order to maintain the appropriate level of the different biogeochemical components within the system over long term simulations. The skill of the model is demonstrated using in situ data, satellite data and estimated fluxes from different studies based on observational data. The model reproduces the main features of the system such as the phytoplankton bloom, hypoxic areas, pH and calcium carbonate saturation states. The model also reproduces well the estimated transport of nitrate from one region to the other. We revisited previous estimates of the riverine nutrient contribution to surface nitrate in the Lower St. Lawrence Estuary using the model. We also explain the mechanisms that lead to high ammonium concentrations, low dissolved oxygen, and undersaturated calcium carbonate conditions on the Magdalen Shallows.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.189
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations34
Published2021
Admission routes2
Has abstractyes

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