Assessment of vanadium and nickel enrichment in Lower Athabasca River floodplain lake sediment within the Athabasca Oil Sands Region (Canada)
Bibliographic record
Abstract
Sediment quality monitoring is commonly used to assess for river pollution by industrial activities, but requires knowledge of pre-disturbance conditions. This has long been a critical knowledge gap for assessing pollution of the Lower Athabasca River within the Athabasca Oil Sands Region (AOSR) because sediment quality monitoring started 30 years after mining operations began in 1967. Here, we analyze oil-sands pollution indicator metals vanadium (V) and nickel (Ni) in sediment cores from five Athabasca River floodplain lakes spanning from 17 km upstream to 58 km downstream of central oil sands operations. These data are used to define pre-development baseline (i.e., reference) concentrations and assess for enrichment in sediment deposited after 1967. Measurements of organic and inorganic matter content were used to differentiate periods of strong and weaker Athabasca River influence in the sediment records, as needed to discern pathways of metal deposition. Numerical analyses reveal that post-1967 V and Ni enrichment factors have remained below the 1.5 threshold for 'minimal enrichment' (sensu Birch, 2017) in stratigraphic intervals of strong river influence in the floodplain lakes. Thus, concentrations of V and Ni carried by Athabasca River sediment have not become measurably enriched since onset of oil sands development, as demonstrated by our before-after study design with >99.99% power to detect a 10% increase above pre-development baselines. At the closest lake (<1 km) to oil sands operations, however, enrichment factors for V and Ni increased to 2.1 and 1.5, respectively, in the mid-1980s and have remained at this level when river influence was weaker, indicating contamination via atmospheric transport. Localized enrichment within the oil sands region via atmospheric pathways is a greater concern for ecosystems and society than local and far-field transport by fluvial pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".