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Record W4386442324 · doi:10.5194/egusphere-2023-2014

Persistence of a Subsurface Water Mass in a Deep Mid-Latitude Fjord

2023· preprint· en· W4386442324 on OpenAlexafffundabout
Laura Bianucci, Jennifer M. Jackson, Susan E. Allen, Maxim V. Krassovski, Ian Giesbrecht

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaFisheries and Oceans Canada
FundersFisheries and Oceans CanadaHakai Institute
KeywordsFjordOceanographyWater massEstuarine water circulationAdvectionDetritusEnvironmental scienceGeologyArcticForcing (mathematics)EstuaryClimatology

Abstract

fetched live from OpenAlex

Abstract. Fjords are common geomorphological coastal features in the mid- and high-latitudes, carved by glacial erosion. These deep nearshore zones connect watersheds and oceans, typically behaving as an estuary. Many fjords in the world have shown concerning warming and deoxygenation trends in their deep waters, sometimes at faster rates than the open ocean. While that is the case in several fjords of British Columbia (BC), Canada, some of the same fjords have shown that strong Arctic outflow wind events in winter can lead to cooling and reoxygenation of subsurface waters, with effects lasting until the following autumn. The latter was observed in Bute Inlet, BC in 2019. We used a high-resolution, three-dimensional ocean model to investigate the mechanisms allowing for the persistence of these subsurface conditions through the year. The presence of the subsurface cold water mass reduced the already weak residual circulation, changing its vertical structure from three to four layers. The reduction of mixing and advection allowed for the water mass to stay in place until autumn conditions arrived (i.e., strong wind mixing and reduced freshwater forcing). The identification of mechanisms that allow for the persistence of cold and oxygenated conditions are key to understand potential areas of ecological refugia in a warming and deoxygenating ocean.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.106
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.236
Teacher spread0.204 · 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 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

Citations1
Published2023
Admission routes3
Has abstractyes

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