Assessment of seven Canadian rivers in relation to stages in oil sands industrial development, 1972–2010
Bibliographic record
Abstract
We compiled a 38 year dataset (1972 to 2010) to evaluate changes in water chemistry over time in relation to oil sands mining activities. We examined patterns in three focal elements (dissolved selenium, dissolved arsenic, total vanadium) at 96 sites along seven tributaries of the Athabasca and Clearwater Rivers. Concentrations of the three focal elements did not differ between sites sampled upstream versus downstream of future development, between sites upstream versus downstream of the primary bitumen-bearing geologic formation, or at an upstream site over a 33 year period on the Muskeg River (1976–2009). Hence, longitudinal patterns observed in water chemistry following mine development cannot be attributed to natural background variation. In contrast, concentrations and loads of the three focal elements were greater post-development compared to reference values (p ≤ 0.01), and were typically greatest during the early exploration and land clearing stage of mine development (p < 0.01). Positive correlations between our three focal elements and 17 other variables either associated with bitumen or considered priority pollutants (r ≥ 0.13, p ≤ 0.04) suggest that the historical impacts of oil sands development on water quality extend beyond our three focal parameters. These results indicate that erosion and subsequent runoff associated with land clearing, construction, and early operational activities have affected water quality in the oil sands region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".