Macroinvertebrate biodiversity of the Lower Athabasca River: assessing impacts of multiple stressors
Bibliographic record
Abstract
Currently, a need exists to assess the biological significance of distinct stressors related to groundwater inputs in the Lower Athabasca River (LAR). I used traditional taxonomy supplemented with metabarcoding to conduct a bioassessment of the LAR benthic macroinvertebrate (BMI) communities. Using traditional taxonomy, I identified BMIs at the family level (or lower) at sites exhibiting either low or high conductivity in both upstream, downstream and industry adjacent loci. I used metabarcoding as a complementary approach to traditional taxonomy and established a criterion to provide an efficient methodology for incorporating the two techniques. However, due to the quality of DNA in the pooled barcoded samples, I could not sequence any samples to generate a second, complementary, community data set. Results from traditional taxonomy alone found no relationship between diversity and conductivity or location. I observed that conductivity was associated with the evenness of taxa present at sites represented by reference sites upstream and industry adjacent oil sands sites as well as between reference sites upstream and saline downstream sites. Rank abundance distributions of BMI families did not fit generally accepted theoretical models of reference and stressed conditions for low and high conductivity sites, respectively. Lastly, I did not observe a difference in the composition of taxa between communities across all site types. Results of my study suggest that groundwater inputs from natural, municipal or industrial sources may influence the composition of taxa in different ways, but do not affect overall diversity or evenness of benthic macroinvertebrate communities. This research did not detect a difference in biodiversity between reference sites and sites impacted by municipal, industrial or natural inputs.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".