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Okeanos Explorer Sound Speed Sensor Deployed 2015-01-01

2015· dataset· en· W6890385439 on OpenAlexaboutno aff

Bibliographic record

VenueOcean Networks Canada Society · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)Measure (data warehouse)Software deploymentSpeed of soundBuoyHigh resolution

Abstract

fetched live from OpenAlex

The Sound Velocity Profiler on Okeanos Explorer was deployed on 2015-01-01 on Okeanos Explorer. The Okeanos Explorer is operated by the National Oceanic and Atmospheric Administration (NOAA) Office of Ocean Exploration and Research (OER). This device is a Sound Speed Sensor. The sound speed in water, such as at the ocean surface or surrounding a remote operated vehicle (ROV), is measured by sensors that use "time of flight" technology to provide low noise, high accuracy and high resolution data. Time of Flight (TOF) describes various methods and sensors that measure the time it takes for an object, particle or wave to travel a distance through a medium. Sound speed sensors are often installed on ships flow-through systems and on ROVs. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.325
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.249
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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Same venueOcean Networks Canada SocietyFrench-language works237,207