Zooplankton communities are good indicators of potential impacts of Athabasca oil sands operations on downwind boreal lakes
Bibliographic record
Abstract
We used zooplankton communities as indicators to evaluate the potential influence of acidifying–eutrophying emissions from the Athabasca oil sands region (AOSR) on 244 downwind lakes in northwest Saskatchewan. The impacts of regional environmental change on zooplankton communities are determined by responses of resident species to altered local environmental conditions as well as changes in composition due to dispersal processes. To test and quantify the relative importance of these individual processes, we conducted ordination analyses, spatial modeling, and variation partitioning. Local environmental factors were the dominant determinants of community structure, including two major environmental gradients susceptible to atmospheric emissions (i.e., acid–base status and productivity). Spatial structuring of these factors induced similar spatial structures in zooplankton distribution across the region. However, disentangling any impacts of the AOSR on these environment–spatial–species relationships from the underlying natural variability was precluded by unavailability of baseline data. Nevertheless, as our findings indicate that dispersal of zooplankton was not strongly limiting across this broad geographic region, zooplankton indicators can be crucial to detect future environmental changes in lakes across northwest Saskatchewan.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".