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Record W2324503803 · doi:10.18063/som.2016.01.002

Annual sea level variations off Atlantic Canada from satellite altimetry

2016· article· en· W2324503803 on OpenAlexaboutno aff
Guoqi Han

Bibliographic record

VenueSatellite Oceanography and Meteorology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsTide gaugeAnnual cycleAltimeterSea levelOceanographySea-surface heightWater columnClimatologySalinityTemperature salinity diagramsGulf StreamEnvironmental scienceGeologyGeodesy

Abstract

fetched live from OpenAlex

Annual cycle of sea level off Atlantic Canada has been investigated based on a merged satellite altimetry dataset and a monthly temperature and salinity dataset. The altimetric results were compared with coastal tide-gauge data and steric height calculated from the temperature and salinity dataset. There was a general north-south variation in the amplitude of the altimetric annual cycle, increasing from 4 cm in the Labrador Sea to 15 cm in the Gulf Stream and the North Atlantic Current Region. The annual cycle in the deep ocean can approximately be accounted for by the steric height variability relative to 700 m, in which the thermosteric effect was the dominant contributor. The halosteric effect over the continental slope, especially over the northern Labrador Slope was also important. While the thermosteric effect occurred dominantly at the top 100 m water column, there was substantial halosteric variation in the 100–300 m water column. The annual sea level cycle along the Canadian Atlantic coast showed a complicated pattern in amplitude, but the phase was highly coherent with the highest sea level in fall. The steric height accounts for a substantial portion of the coastal annual cycle, but other factors such as wind forcing may be equally important.

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 score0.286
Threshold uncertainty score0.667

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 head0.015
GPT teacher head0.194
Teacher spread0.178 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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