Assessing changes in indicators of fish health measured between 1997 and 2019 relative to multiple natural and anthropogenic stressors in Canada’s oil sands region using spatio-temporal modeling
Bibliographic record
Abstract
Industrial development in Canada’s oil sands region influences the ambient environment. Some of these influences, such as the atmospheric deposition of emitted particles and gases are well-established using chemical indicators, but the effects of this process on bioindicators examined in field studies are less well-supported. This study used an extensive dataset available from 1997 to 2019, spatio-temporal modeling (Integrated Nested Laplace Approximation), and data on industrial and non-industrial covariates, including deposition patterns estimated using HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) to determine if changes in sentinel fishes collected in streams from Canada’s Oil Sands Region were associated with oil sands industrial activity. While accounting for background variables (e.g., precipitation), estimated deposition of particles emitted from mine fleets (e.g., Aurora North), in situ stacks (e.g., Primrose and Cold Lake), mine stacks (e.g., Kearl), mine dust (e.g., Horizon), road dust (e.g., Muskeg River mine), land disturbance in hydrologically-connected areas, and wildfires were all associated with at least one fish endpoint. While many individual industrial stressors were identified, a specific example in this analysis parallels other work: the potential influence of emissions from both Suncor’s powerhouse and dust emitted from Suncor’s petroleum coke pile may both negatively affect fish health. Comparisons of fitted values from models with the estimated industrial effects and with deposition rates set to zero suggested some negative (and persistent) influences of atmospheric deposition at some locations, such as the gonadosomatic index (GSI) in the lower Muskeg and Steepbank rivers. While there is evidence of some large differences at individual locations the mean GSI and body condition estimates have improved throughout the region since the beginning of these collections in the late 1990s potentially highlighting improved environmental performance at the facilities, widespread enrichment effects, or interactions of stressors. However, mean liver-somatic indices have also slightly increased but remain low. These results, coupled with others suggest the utility of spatio-temporal approaches to detect the influence and effects of oil sands development at both local and regional scales.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".