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Record W2596732580

Remote monitoring of physical and biological properties in the Salish Sea: VENUS sea-surface monitoring with high frequency radar and instrumented ferries

2016· article· en· W2596732580 on OpenAlexaboutno aff
Akash R. Sastri, Richard Dewey, Steve Mihály, Kevin S. Bartlett

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsVenusRemote sensingRadarEnvironmental scienceGeologyMeteorologyOceanographyAstrobiologyGeographyEngineeringAerospace engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Remote monitoring of sea surface properties is a key objective of the VENUS coastal observatory operated by Ocean Networks Canada (ONC; www.oceannetworks.ca). Our focus is on the physically and biologically dynamic southern central Strait of Georgia where the Fraser River discharges into the Salish Sea. Here we discuss our experiences with: 1) a high frequency radar installation (Coastal Ocean Dynamics Applications Radar, CODAR); and 2) three instrumented ferry routes (starting in 2012). The CODAR system was deployed with two antennae on either side of the mouth of the Fraser River in 2011. This particular arrangement provides for hourly measurements of both radial and total surface current velocities in the vicinity of and including the Fraser River plume. Similarly, each of the three instrumented BC-Ferries routes transit through the Fraser River plume several times per day en route between Vancouver Island and the mainland. Measurements of salinity, temperature, phytoplankton biomass, dissolved oxygen, and coloured dissolved organic matter are available every 10 seconds (in real-time on the internet) and enable high-resolution, spatio-temporal characterization of sea-surface properties. We will present an overview of these relatively new time-series in the context of data products available to stakeholders in the Salish Sea.

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 score0.006
Threshold uncertainty score0.646

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 head0.025
GPT teacher head0.188
Teacher spread0.163 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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