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Record W2979885855 · doi:10.2989/1814232x.2019.1656105

Localised intermittent upwelling intensity has increased along South Africa’s south coast due to<i>El Niño</i>–Southern Oscillation phase state

2019· article· en· W2979885855 on OpenAlexaff
Murray I. Duncan, Nicola C. James, Amanda E. Bates, WS Goschen, Warren M. Potts

Bibliographic record

VenueAfrican Journal of Marine Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsUpwellingEl Niño Southern OscillationClimatologyOceanographyAnomaly (physics)Environmental scienceSea surface temperatureMultivariate ENSO indexGeologyLa NiñaPhysics

Abstract

fetched live from OpenAlex

The El Niño–Southern Oscillation (ENSO) phase state is reported to drive interannual variability in sea temperatures along South Africa’s south coast through its influence on wind-induced upwelling processes. Whether ENSO drives the intensity of localised, abrupt, intermittent upwelling is less well known. To explore this relationship, we used an index of localised, extreme (>2 °C anomaly), intermittent upwelling intensity, derived from in situ sea temperature data within the Tsitsikamma National Park Marine Protected Area, and quantified the relationship between annual cumulative upwelling intensities (1991–2013) with an annual ENSO index, namely the Southern Oscillation Index. We found that ENSO phase state modulates the cumulative intensity of extreme intermittent upwelling events during an annual period, with more and greater events during La Niña phases compared with El Niño phases. Furthermore, these extreme upwelling events have increased with time along South Africa’s south coast as ENSO phase state becomes more intense and variable. Our findings support the emerging notion that the biological effects of climate change may be manifested through increased environmental variability rather than long-term mean environmental changes as ENSO is predicted to remain the dominant driver of local climate patterns in the future.

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)0.002
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 score0.239
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.212
Teacher spread0.194 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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