A bioassessment of lakes in the Athabasca Oil Sands Region, Alberta, using benthic macroinvertebrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it