Evaluating temporal patterns of metals concentrations in floodplain lakes of the Athabasca Delta (Canada) relative to pre-industrial baselines
Bibliographic record
Abstract
Sediment quality monitoring is widely used to quantify extent of river pollution, but requires knowledge of pre-disturbance conditions in the potentially altered landscape. This has long been identified as a critical aspect to develop for addressing concerns of river pollution in the Alberta Oil Sands Region. Here, we use analyses of sediment cores from eight floodplain lakes spanning a 67 river-km transect across the Athabasca Delta to define pre-1920 (pre-industrial) baseline concentrations for vanadium and five primary pollutants. We then evaluate if sediment metals concentrations have become enriched above baseline since onset of oil sands development and other industrial activities. Results demonstrate no enrichment of metals concentrations (except zinc at one lake) and absence of consistent temporal increases above pre-industrial baselines. Thus, natural processes continue to dominate metal deposition in floodplain lakes of the Athabasca Delta -- an important finding to inform stewardship decisions. The pre-1920 metals concentrations baselines offer a useful tool for ongoing sediment monitoring in aquatic ecosystems of the Athabasca Delta.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".