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Record W2954080295 · doi:10.1080/07038992.2019.1581056

Canada’s Contributions to the SWOT Mission – Terrestrial Hydrology(SWOT-C TH)

2019· article· en· W2954080295 on OpenAlexafffundvenueabout
Alain Pietroniro, Daniel L. Peters, Daqing Yang, J. M. Fiset, Robert Saint-Jean, Vincent Fortin, Robert Leconte, Jean Bergeron, Gabriela Llanet Siles, Mélanie Trudel, Camille Garnaud, Pascal Matte, L. C. Smith, Colin J. Gleason, Tamlin M. Pavelsky

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversité de SherbrookeCanadian Space AgencyUniversity of VictoriaEnvironment and Climate Change Canada
FundersCanadian Space AgencyEnvironment and Climate Change Canada
KeywordsSWOT analysisOcean surface topographyGeographySatelliteEarth observationContext (archaeology)Environmental resource managementRemote sensingMeteorologyEnvironmental scienceBusinessEngineeringGeodesy

Abstract

fetched live from OpenAlex

The origins of the Surface Water and Ocean Topography (SWOT) mission date back to the mid-1970s with the launch of GOES-3 and SEASAT. These missions were then followed in 1992 by the Topex-Poseidon satellite, then by Jason-1 (2001), OSTM/Jason-2 (2008), and Jason 3 (2016), a series of joint satellite missions between NASA and CNES with a goal to monitor global ocean circulation. The proposed new SWOT mission will provide 120-km-wide swath interferometric coverage with a 20-km-wide gap at the nadir. The SWOT measurements will consist of water surface elevations and water surface slopes covering nearly all of the earth’s land surface at least once every 21 days. In 2010, NASA invited the Canadian Space Agency to contribute, and Canadian scientists welcomed the invitation to join the SWOT Science Definition Team and contribute to the experiments. The Canadian segment of the mission is known as the “SWOT-C” project. The SWOT satellite mission will provide unique opportunities in the Canadian context for water managers in both the public domain and in the private sector. This paper provides an overview of recent scientific progress by the SWOT-C Terrestrial Hydrology team, outlining current plans and progress towards applications and calibration post-launch.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.318

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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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 designNot applicable
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

Citations15
Published2019
Admission routes4
Has abstractyes

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