Exploring the Influence of Industrial and Climatic Variables on Communities of Benthic Macroinvertebrates Collected in Streams and Lakes in Canada’s Oil Sands Region
Bibliographic record
Abstract
Identifying and tracking the influence of industrial activities on streams and lakes is a priority for monitoring in Canada’s oil sands region (OSR). While differences in indicators are often found in waterbodies adjacent to mining facilities, the confounding influence of natural exposures to bitumen and other stressors can affect the identification of industrial effects. However, recent work suggests metrics of industrial activity at individual facilities, including production and fuel consumption, may be used in site-specific analyses to identify influence of the industry as a whole as well as individual operations. This study further examined the potential relationships between industrial and climatic variables on benthic communities from 13 streams and 4 lakes using publicly available data from the minable region and the Elastic Net (EN) variable selection technique. From the full set of possible industrial and climate variables, the EN commonly identified the negative influence of plant and fuel use of petroleum coke at the Suncor Basemine on benthic communities in streams and lakes. The fuel/plant use of petroleum coke at Suncor likely reflects the emission and regional deposition of delayed coke fly ash. Among the other industrial variables, crude bitumen production at Syncrude Mildred Lake and other facilities, steam injection rates, and petroleum coke stockpiling were also selected for some benthic invertebrate indices at some sites. Land disturbance metrics were also occasionally selected, but the analyses largely support the predominant influence of industrial facilities via (inferred) atmospheric pathways. While climate variables were also commonly selected by EN and follow-up work is needed, this study suggests that integrating industrial performance data into analyses of biota using a site-specific approach may have broad applicability in environmental monitoring in the OSR. More specifically, the approach used here may both resolve the long-standing challenge of natural confounding influences on monitoring the status of streams in the OSR and track the influence of industrial activities in biota below critical effect sizes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".