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Record W1925173476

The selection, integration, and evaluation of a payload for chemical plume detection on an autonomous underwater vehicle

2003· dissertation· en· W1925173476 on OpenAlexaboutno aff
Vanessa Pennell

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2003
Typedissertation
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsPayload (computing)UnderwaterEnvironmental scienceSampling (signal processing)Sample (material)Environmental monitoringMarine engineeringEngineeringScientific instrumentEnvironmental engineeringComputer scienceGeographyTelecommunicationsDetector
DOInot available

Abstract

fetched live from OpenAlex

The oil and gas industry is growing off the East Coast of Canada, and as a result the discharges associated with production continue to grow. Mandates are in place to ensure that these activities proceed in an environmentally acceptable manner. Policies are being introduced worldwide that are leading to zero-discharge or the use of complex risk assessment processes to determine the environmental impacts of these discharges. Little real data exists about the environmental effects of these discharges, and new and innovative methods for data collection need to be considered. One possibility is to use autonomous underwater vehicles (AUV) for environmental monitoring, which is explored in this thesis. -- The steps associated with choosing and implementing an environmental payload on an AUV are discussed in this thesis. It was determined that sample collection methods were not appropriate for use on an AUV, but that in-situ sensors were useful for collecting environmental data. Sea-trials were performed in Burrard Inlet, British Columbia between February 4 and February 6, 2002. The AUV used for this project was the ARCS vehicle, supplied by International Submarine Engineering in British Columbia. The payload for this mission used two in-situ instruments, which were supplied by Applied Microsystems Ltd. The first was an underwater mass spectrometer called the "In-Spectr". It is capable of measuring dissolved gases and volatile organic chemicals to atomic masses of up to 200 atomic mass units. It was used in a continuous mode of sampling that indicated the presence or absence of a chemical. The second instrument measured conductivity, temperature, and depth (CTD) and was called the "Micro-CTD". These instruments were user-friendly and easily integrated into the ARCS vehicle. -- The trials were successful in demonstrating the use of an underwater mass spectrometer on an AUV. It was shown that the payload could be used to detect chemical plumes, which might be considered a viable option for environmental monitoring offshore. A chemical tracer, dimethyl sulphide (DMS), was pumped into the water at a maximum rate of 9 L/h, as stipulated by the BC Ministry of Water, Land and Air Protection. The mass spectrometer successfully detected the location of the DMS in the water ±13.5 m. The CTD was used to complement this data. Temperatures in the Inlet were relatively constant, with variations within one degree Celsius. Salinity increased with depth. The fresher water on the surface could be attributed to the larger volumes of precipitation during the winter months in that geographic region. -- The mass spectrometer was highly sensitive to power interruptions, which forced the instrument to shutdown. It was recommended that a back-up power system be provided. It was also recommended that the internal clocks on all instruments be synchronized before the sea-trials to simplify data analysis.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.022
GPT teacher head0.268
Teacher spread0.246 · 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".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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