A bioassessment of lakes in the Athabasca Oil Sands Region, Alberta, using benthic macroinvertebrates
Bibliographic record
Abstract
Emissions of sulphur oxides, nitrogen oxides and other pollutants have increased in the Athabasca Oil Sands Region (AOSR) in Alberta, Canada. Atmospheric pollutants impact aquatic communities through a number of processes, but due to a lack of regional monitoring programs potential biological impacts have not been assessed. In this study, a bioassessment was conducted using approaches borrowed from a variety of protocols to establish a baseline dataset, determine appropriate methodologies, and to assess the current impact of emissions on benthic macroinvertebrate (BMI) communities in the AOSR. As a result, 32 lakes, including 5 test lakes located in a modelled high deposition region, were sampled for water chemistry and BMI. The Reference Condition Approach (RCA) was used because a baseline dataset does not exist and data were evaluated using three separate statistical techniques. All of the statistical methods used: One Sample T-Tests, Multivariate Analysis of Variance (MANOVA) and Test Site Analysis (TSA), showed that BMI assemblages in test lakes differed from BMI assemblages in reference lakes. Traditional statistics classified all 5 test lakes as "significantly impaired" whereas TSA identified 3 of the 5 test lakes as only potentially impaired and 2 lakes were in "reference condition". The variability in lake attributes present challenges in interpreting BMI data and establishing an accurate biomonitoring program in the AOSR which need to be addressed in future assessment studies.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".