Geospatial analysis of oil discharges observed by the National Aerial Surveillance Program in the Canadian Pacific Ocean
Bibliographic record
Abstract
Oil pollution resulting from day to day human maritime activities contributes a high portion of the overall input into marine environments, constituting a major threat to marine ecosystems worldwide. In Canada, the National Aerial Surveillance Program (NASP) extensively monitors and collects information on oily discharges using remote sensing devices. Despite the availability of data from NASP and other surveillance programs internationally, there is a paucity of spatial analyses of oil pollution patterns, particularly in their association with human marine pursuits. The objective of this paper is to analyze the association between observed oily discharges and human maritime activities in the Canadian Pacific Ocean. This study used Poisson regression to spatially model detected oily discharges with marine traffic, coastal facilities and proximity to coast. Further, it developed localized (‘regional’) models to address spatial heterogeneity. The models identify recreational activities, passenger traffic, commercial traffic, fisheries, and proximity to the coast as predictors of observed oily discharges. The regional models yield more accurate and reliable estimates of local associations, and identify more parsimonious sets of predictors for each region. By identifying and accounting for human activities most associated with oily discharge patterns, the models developed in this study could be used to estimate pollution rates in areas with less surveillance, and identify areas where NASP coverage may need to be increased. Spatially explicit rates estimated by these models can be used to monitor the effectiveness of programs and policy aimed at reducing discharge rates of oily pollution. This study can be used as a model approach for extending the analysis to the other coasts of Canada, using available NASP data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".