“Commercially Sensitive” Environmental Data: A Case Study of Oil Seep Claims for the Old Harry Prospect in the Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
We expose the difficulties we encountered to obtain from industry environmental information that is crucial for impact studies and decision-making related to the potential development of offshore oil and gas in the Gulf of St. Lawrence, Canada. This case concerns the information disseminated by the oil company Corridor Resources that there are six persistent, natural oil seeps emanating from the flanks of the Old Harry geological structure in the Gulf of St. Lawrence. According to Corridor, these seeps rise through the water column and appear at the sea surface directly above the prospect, forming permanent oil slicks visible from satellite imagery. Corridor believes this is an indication that the Old Harry prospect contains oil. While this information might be credible, it has been impossible for us to verify its accuracy because the sources are kept secret under the argument of “commercially sensitive.” Yet, such information about the possible presence of natural oil and its sources is essential to obtain and to verify in order to construct a reliable baseline initial state against which any new man-made oil contribution resulting from eventual oil and gas development could be compared with, and impacts on the marine environment, ecosystem, and people be then truly assessed. We describe the legal, economic, and political contexts in which withholding this information might happen, and we take a critical look at its impact on scientific research as well as on decision-making in society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".