Innovative approaches to environmental effects monitoring using an autonomous underwater vehicle
Bibliographic record
Abstract
An overview is given of a project to develop autonomous underwater vehicle (AUV) technology for environmental effects monitoring (EEM) in the offshore oil and gas industry. This project is a joint venture between the Institute for Marine Dynamics of the National Research Council Canada (NRC-IMD) and the Ocean Engineering Research Centre at Memorial University of Newfoundland (MUN-OERC), with the support of several Canadian companies and universities. With the offshore oil and gas industry growing rapidly, it is important that new and innovative methods for EEM be considered. The paper reports on results from the project "Offshore Environmental Risk Engineering using Autonomous Underwater Vehicles" (OERE-AUV). The results include: (a) the development of a new general-purpose test-bed AUV called "C-SCOUT", (b) the planning for a series of sea trials using an existing vehicle to determine the effectiveness of an AUV to delineate a near-shore ocean outfall, (c) the characteristics and performance of several candidate sensors for EEM, and, (d) the development of hydrodynamic dispersion models for discharges into a marine environment. The ultimate application of the research is for the EEM of discharges of produced water, drilling cuttings and drilling muds from offshore oil and gas production facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".