A catalogue of potential natural oil seeps in the marine environment of Hudson Bay-Hudson Strait and Foxe Channel from 2015-2017 RADARSAT-2 imagery analysis
Bibliographic record
Abstract
We report results from the analysis of Radarsat-2 images for the detection of suspected oil slicks in the marine environment of Hudson Bay/Strait and Foxe Channel. 1278 images were acquired during the falls of 2015, 2016 and 2017. The potential slick candidates were identified using two methods: visual interpretation and semi-automated interpretation. The visual method is similar to the ones described in Decker et al. (2013a, b). The semi-automated approach is based on a suite of algorithms designed to detect and characterize dark areas. Both methods make use of wind speed and chlorophyll-a data. A total number of 33 oil slicks candidates are reported with their locations and corresponding images. The ultimate goal of the multi-temporal aspect of the project was to look for persistence over time of seep candidates concentrated over a same region in order to assist in finding regions with a greater likelihood of oil seep origin. The current survey does not convincingly support the oil seep origin of any detected dark spot but may help future works focus on the few areas that show more dense occurrences of slick candidates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".