Impact of sample collection on prokaryotic and eukaryotic diversity of niche environments of the oil-sand mining impacted Athabasca River
Bibliographic record
Abstract
Microbial communities are an important aspect of overall riverine ecology; however, appreciation of the effects of anthropogenic activities on unique riverine microbial niches, and how the collection of these samples affects the observed diversity and community profile is lacking. We analyzed prokaryotic and eukaryotic communities from surface water, biofilms, and suspended load niches along a gradient of oil sands-related contamination in the Athabasca River (Alberta, Canada), with suspended load or particle-associated communities collected either via Kenney Sampler or centrifugation manifold. At the phylum level, different niche communities were highly similar to each other and across locations. However, there were significant differences in the abundance of specific genera among the different niches and across sampling locations. A generalized linear model revealed that use of the Kenney Sampler resulted in more diverse bacterial and eukaryotic suspended load community than centrifugal collection, though suspended load communities collected by any means remained stably diverse across locations. Although there was an influence of water quality parameters on community composition, all sampled sites support diverse bacterial and eukaryotic communities regardless of the degree of contamination, highlighting the need to look beyond ecological diversity as a means of assessing ecological perturbations, and consider collecting samples from multiple niche environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".