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Long-term stability of a new conductivity-temperature sensor tested on the VENUS cabled observatory

2010· article· en· W1998054239 on OpenAlexaffabout
Tomohiro Horiuchi, Fabian Wolk, Paul Macoun

Bibliographic record

VenueOCEANS'10 IEEE SYDNEY · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsObservatoryVenusConductivityCalibrationRemote sensingFoulingMooringEnvironmental scienceMaterials scienceElectrical engineeringMarine engineeringEngineeringGeologyPhysicsAstrobiologyMembraneChemistryAstronomy

Abstract

fetched live from OpenAlex

The stability of the conductivity sensor is a key consideration for the long-term deployments of the instruments on mooring and observatories, because the conductivity measurement is very sensitive to the accumulation of organisms (bio-fouling) inside the sensor. We tested the performance of a conductivity sensor, the ALEC CTW, which features a simple but effective wiper mechanism to keep the sensing cavity of the conductivity cell free of bio-fouling. The sensor was deployed for a period of 12 months on the Victoria Experimental Network Under the Sea (VENUS) observatory, operated by the University of Victoria in British Columbia, Canada. The VENUS observatory provided power and data telemetry to monitor the sensor's performance in real time. A post recovery calibration of the conductivity sensor showed that the wiper mechanism was effective in maintaining the sensors calibration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.053
GPT teacher head0.264
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations6
Published2010
Admission routes2
Has abstractyes

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Same venueOCEANS'10 IEEE SYDNEYSame topicWater Quality Monitoring TechnologiesFrench-language works237,207