Assessing Hydrocarbon presence in the waters of Port au Port bay, Newfoundland and Labrador, for AUV oil spill delineation tests
Bibliographic record
Abstract
The waters adjacent to the Port au Port Peninsula, in Port au Port Bay, Newfoundland andLabrador, are known to be subject to release of hydrocarbons from natural oil seeps and oldabandoned oil wells. An investigation was done to determine whether there were sufficient oilcompounds present for planned autonomous underwater vehicle (AUV) test missions to developadaptive sampling algorithms to delineate oil spills. Fluorometers were used in-situ to measureoil concentrations. Oil-and-water samples were taken at selected waypoints for chemical analysisin the laboratory to validate the sensor measurements and to provide a ground truth. Only oneof the fluorometers was found to have a minimum detection level that was capable of sensingthe hydrocarbons in the water column. The water sample results indicated hydrocarbon levelsup to almost 30 ppm in the east side of the bay, just to the west of Shoal Point, but no detectablelevels on the west side of the bay. It was concluded that it would be possible to operate an AUVon a planned fixed mission with a pre-programmed search path and record the levels of signaldetected from fluorometers or other sensors. However, it would be difficult to implement anadaptive mission in this case because of the low levels of sensor signals resulting from the lowconcentrations of hydrocarbon present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".