The Use of a RADARSAT-Derived Long-Term Dataset to Investigate the Sea Surface Expressions of Human-Related Oil Spills and Naturally Occurring Oil Seeps in Campeche Bay, Gulf of Mexico
Bibliographic record
Abstract
. Campeche Bay, located in the Gulf of Mexico, is a well-established fossil fuel producing region, with numerous oil rigs exploring oil and natural gas. In an effort to reduce negative impacts on marine ecosystems, Pemex continuously monitored Campeche Bay for oil slicks, i.e., naturally occurring oil seeps and manmade oil spills. A long-term dataset (2000–2012) of synthetic aperture radar measurements from both RADARSAT satellites (766) is leveraged to investigate the spatial-temporal distribution of oil slicks (14,210) in this region. The present study has a threefold goal: (1) describe the monitoring strategy completed by Pemex and the information produced during such monitoring; (2) investigate the spatial-temporal distribution of the oil slicks observed in Campeche Bay, centering on aspects related to their occurrence; and (3) demonstrate the usefulness of RADARSAT-derived information in the execution of effective long-term environmental applications to locate seeps and spills on the sea surface. The observations confirm the massive oil input contribution of the Cantarell Oil Seep to the Campeche Bay. Oil spills (96%) usually occur in water depths shallower than 100 m, whereas oil seeps (63%) commonly occur in waters deeper than 1,000 m. The successful long-term application of RADARSAT-derived information has been shown.
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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) | 0.000 | 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".