Overview of Existing Science to Inform Oil Sands Process Water Release: A Technical Workshop Summary
Bibliographic record
Abstract
The extraction of oil sands from mining operations in the Athabasca Oil Sands Region uses an alkaline hot water extraction process. The oil sands process water (OSPW) is recycled to facilitate material transport (e.g., ore and tailings), process cooling, and is also reused in the extraction process. The industry has expanded since commercial mining began in 1967 and companies have been accumulating increasing inventories of OSPW. Short- and long-term sustainable water management practices require the ability to return treated water to the environment. The safe release of OSPW needs to be based on sound science and engineering practices to ensure downstream protection of ecological and human health. A significant body of research has contributed to the understanding of the chemistry and toxicity of OSPW. A multistakeholder science workshop was held in September 2017 to summarize the state of science on the toxicity and chemistry of OSPW. The goal of the workshop was to review completed research in the areas of toxicology, chemical analysis, and monitoring to support the release of treated oil sands water. A key outcome from the workshop was identifying research needs to inform future water management practices required to support OSPW return. Another key outcome of the workshop was the recognition that methods are sufficiently developed to characterize chemical and toxicological characteristics of OSPW to address and close knowledge gaps. Industry, government, and local indigenous stakeholders have proceeded to utilize these insights in reviewing policy and regulations. Integr Environ Assess Manag 2019;15:519-527. © 2019 SETAC.
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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".