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Record W7105668701 · doi:10.24400/527896/a03-2017.2400

On the spatial scale resolved by the future SWOT KaRIN measurement over the ocean

2017· article· W7105668701 on OpenAlexaff

Bibliographic record

VenueCentre National d’Etudes Spatiales · 2017
Typearticle
Language
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsSemtech (Canada)
Fundersnot available
KeywordsOcean surface topographySea-surface heightAltimeterSWOT analysisSatelliteEddyScale (ratio)Data assimilationImage resolution

Abstract

fetched live from OpenAlex

The Surface Water and Ocean Topography (SWOT) mission aims to measure the sea surface height (SSH) at a high spatial resolution using Ka-band Radar Interferometer (KaRIN). The primary oceanographic objective is to characterize the ocean eddies at a spatial resolution of 15 km for 68% of the ocean. This resolution is derived from the signal to noise ratio between the wavenumber spectrum of the conventional altimeter (projected to submesoscale) and the SWOT SSH errors. While the 15km threshold is useful as a global approximation of the spatial scales resolved by SWOT (SWOT-scale), it can be misleading for regional studies. Here we revisit the problem using a high-resolution (~2km) tide-resolving global ocean simulation and map the SWOT-scale as a function of latitude-longitude and season. The results show that the SWOT-scale has a strong geographic and seasonal dependence. In general, it is smaller (<15km) in low latitudes, increases to ~30km in mid-latitudes; and is larger in local winter than in summer. Internal gravity waves and internal tides have a significant contribution to the scale variation. These characteristics provide a guideline for interpreting the satellite fidelity with ocean physics in consideration, which in turn sheds light on developing the future SWOT data assimilation system.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.002
Scholarly communication0.0010.000
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.219
Teacher spread0.199 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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