Bibliographic record
Abstract
This book began when Kevin Timoney noticed a suspicious pattern in data reported by the Alberta Energy Regulator. For tens of thousands of spills, recovery volumes exactly matched the reported spill volumes. In short, the data were too good to be true. And so began a search for the scientific truth about spills. In western North America crude oil and saline water spills – both small and large – occur daily and cause permanent damage to ecosystems that remains largely hidden from public view. Hidden Scourge takes the reader on a journey into a covert world of energy industry spills with environmental incident data from over 100,000 spills in Alberta, Saskatchewan, North Dakota, Montana, and the Northwest Territories. Timoney evaluates the truthfulness of regulatory reporting in light of evidence from peer-reviewed scientific data, original field observations, industrial and government reports, interviews, and documents obtained under freedom of information. In stark contrast to a halcyon picture of prosperity and "world-class" environmental management, the reality is rampant destruction of biodiversity, persistent soil contamination, failed reclamation, and thousands of undocumented spills. Hidden Scourge grounds existential debates about climate and ecological crises in evidence of how hydrocarbon-based economies change the ecosystems where fossil fuels are extracted. The science is clear: the industry consistently damages ecosystems wherever it operates. If energy-industry regulators cannot act independently, honestly, and in the public interest, they profoundly undermine democratic institutions. The result is a legacy of contaminated sites that will burden future generations with great uncertainty and cost.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.054 | 0.022 |
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".