Canine Oil Detection (K9-SCAT) following 2015 Releases from the T/V Arrow Wreck
Bibliographic record
Abstract
Abstract The T/V Arrow sank in 1970, spilling Bunker C fuel oil into Chedabucto Bay, Nova Scotia. In the summer and fall of 2015, residual oil leaked from the sunken vessel and re-oiled shorelines in the Bay. A K9-SCAT field study, funded by Environment and Climate Change Canada (ECCC), was conducted in June 2016 to assess the capability of detection canines to locate stranded oil following the new releases. The canine detected small amounts of weathered surface oil that were barely visible, and in some cases, not visible, to the SCAT-trained observers, as well as subsurface oil on mixed- and coarse-sediment beaches. The average speed of a survey, in terms of the length of shoreline covered, varied depending on the shore type and the width of the survey band. The most challenging site was a steep bedrock shoreline with an alongshore survey rate of 0.2 linear km/hour. Typical alongshore coverage rates for the wide, mixed sediment were in the range 0.7 to 1.2 linear km/hour, and for both straight, wide sand beaches were 1.2 km/hour. The highest alongshore rate was 2.4 linear km/hour for the narrow beach on Janvrin Island. The successful detection of 2015 T/V Arrow cargo oil (both naturally stranded and intentionally planted) on selected Chedabucto Bay shorelines indicates that there is a low risk, high confidence level that the canine did not miss subsurface oil, although that possibility may exist. Where the canine made an alert and no surface oil was visible, chemical analyses of sediment samples indicated that weathered petroleum hydrocarbons were present at those locations and, therefore, the canine had made correct alerts. The results provide further “proof of concept” for K9-SCAT teams to support surface and subsurface oil detection during traditional shoreline assessment surveys.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".