Evaluating Lower Athabasca River Sediment Metal Concentrations from Alberta Oil Sands Monitoring Programs Using Predevelopment Baselines
Bibliographic record
Abstract
Since 1997, sediment metal concentrations have been monitored in the Alberta Oil Sands Region (AOSR) of the Lower Athabasca River by the Regional Aquatics Monitoring Program (RAMP; 1997-2002), the Joint Oil Sands Monitoring Program (JOSM; 2012-2014), and the Oil Sands Monitoring Program (OSM; 2015-present). However, it has remained difficult to differentiate industrial sources from natural sources and quantify the extent of pollution due to inadequate knowledge of predevelopment reference conditions. Here, baselines were constructed using predevelopment (i.e., pre-1967) sediment concentrations of US EPA priority pollutants (Be, Cr, Cu, Ni, Pb) and V, an element elevated in bitumen and associated waste materials, normalized to Al concentration in cores from floodplain and upland lakes within the AOSR to characterize the natural range of variability. The Lower Athabasca River sediment metal monitoring data were examined in the context of the predevelopment baselines. Most metals are below the threshold for minimal enrichment (<1.5x baseline) except for chromium (up to 4.8x) in some RAMP samples. The predevelopment baselines for sediment metal concentrations will be of particular importance as the oil sands industry potentially shifts from a no-release policy to the treatment and release of oil sands process waters directly to the Lower Athabasca River.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".