MétaCan
Menu
Back to cohort
Record W3009401489 · doi:10.1515/ohs-2020-0008

Marked contrast in wind-driven upwelling on the southeastern Nova Scotia coast in July of two years differing in ENSO conditions

2020· article· en· W3009401489 on OpenAlexaffabout
Ricardo A. Scrosati, Julius A. Ellrich

Bibliographic record

VenueOceanological and Hydrobiological Studies · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsUpwellingOceanographyDownwellingGeologySea surface temperatureNova scotiaClimatology

Abstract

fetched live from OpenAlex

Abstract Upwelling occurs on several coasts of the world, but it has mostly been studied on eastern ocean boundaries. We investigated upwelling on a western ocean boundary for which limited information exists. Using daily in-situ data on sea surface temperature (SST), we found a marked contrast in coastal cooling between July 2014 (pronounced) and July 2015 (weak) at two locations 110 km apart on the Atlantic coast of Nova Scotia, Canada. These findings are consistent with a marked interannual difference in wind-driven upwelling. On the one hand, southwesterlies (which cause upwelling on this coast) were more frequent in July 2014 than in July 2015. On the other hand, Bakun’s upwelling index (which is based on wind data and geographic information) indicated that coastal upwelling was more common and intense in July 2014 than in July 2015, while the reverse was true for downwelling. Interestingly, a strong El Niño event occurred in July 2015, while no El Niño (or La Niña) conditions happened in July 2014. In a recent book evaluating upwelling systems around the world, the system that is the focus of the present study was not included. Therefore, our findings should stimulate future research on upwelling on the Atlantic Canadian coast, in that way helping to further develop the knowledge base for western ocean boundaries.

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 score0.003
Threshold uncertainty score0.353

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 studies0.0000.001
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 head0.050
GPT teacher head0.245
Teacher spread0.195 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueOceanological and Hydrobiological StudiesSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207