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Record W4399862538 · doi:10.1038/s43247-024-01505-5

Deep inflow transport and dispersion in the Gulf of St. Lawrence revealed by a tracer release experiment

2024· article· en· W4399862538 on OpenAlexafffund
Samuel W. Stevens, Rich Pawlowicz, Toste Tanhua, Lennart Gerke, William A. Nesbitt, Adam Drozdowski, Joël Chassé, Douglas W.R. Wallace

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsFisheries and Oceans CanadaDalhousie UniversityUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaMarine Environmental Observation Prediction and Response Network
KeywordsTRACERInflowDispersion (optics)OceanographyEnvironmental scienceGeologyPhysicsOpticsNuclear physics

Abstract

fetched live from OpenAlex

Abstract The Gulf of St. Lawrence is increasingly affected by bottom water hypoxia; however, the timescales and pathways of deep water transport remain unclear. Here, we present results from the Deep Tracer Release eXperiment (TReX Deep), during which an inert SF 5 CF 3 tracer was released inshore of Cabot Strait at 279 m depth to investigate deep inflow transport and mixing rates. Dispersion was also assessed via neutrally-buoyant Swish floats. Our findings indicate that the tracer moves inland at 0.5 cm s −1 , with an effective lateral diffusivity of 2 × 10 2 m 2 s −1 over 1 year. Simplified 1D simulations suggest inflow water should reach the estuary head in 1.7 years, with the bulk arriving after 4.7 years. Basin-wide effective vertical diffusivity is around 10 −5 m 2 s −1 over 1 year; however, vertical diffusivity increases near the basin slopes, suggesting that turbulent boundary processes influence mixing. These results are compared to Lagrangian simulations in a regional 3D model to evaluate the capacity to model dispersion in the Gulf.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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