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Record W1964201649 · doi:10.1029/2005gl023642

A regional index of northeast Pacific variability based on satellite altimeter data

2005· article· en· W1964201649 on OpenAlexaff
Patrick F. Cummins, Gary Lagerloef, Gary T. Mitchum

Bibliographic record

VenueGeophysical Research Letters · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsSea-surface heightClimatologyPacific decadal oscillationSea surface temperatureAltimeterLa NiñaPrincipal component analysisSatelliteEnvironmental scienceGeologyOceanographyEl Niño Southern OscillationRemote sensing

Abstract

fetched live from OpenAlex

An index of climate variability to monitor the state of the upper ocean is proposed for the northeast (NE) Pacific Ocean based on sea surface height (SSH) data from satellite altimetry. While sea surface temperature (SST) is often used characterize ocean variability, SSH reflects the integrated influence of temperature and salinity anomalies through the water column. A canonical correlation analysis shows that SSH and SST anomalies vary coherently at large spatial scales and low frequencies over the region. SSH anomalies are less subject to short period variability and the temporal components for SSH resemble smoothed, low‐pass‐filtered versions of the SST components. Also examined is the relationship between the SST‐based Pacific Decadal Oscillation (PDO) index and the large‐scale, upper ocean variability reflected in the leading principal component of SSH anomalies. The comparison demonstrates that the SSH principal component provides a robust index of regional climate variability that is less noisy than the PDO. The results are used to examine the 1998–2002 climate event over the NE Pacific.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.079
GPT teacher head0.324
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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

Citations52
Published2005
Admission routes1
Has abstractyes

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