Next-Generation Sequencing of Microbial Communities in the Athabasca River and Its Tributaries in Relation to Oil Sands Mining Activities
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
The Athabasca oil sands deposit is the largest reservoir of crude bitumen in the world. Recently, the soaring demand for oil and the availability of modern bitumen extraction technology have heightened exploitation of this reservoir and the potential unintended consequences of pollution in the Athabasca River. The main objective of the present study was to evaluate the potential impacts of oil sands mining on neighboring aquatic microbial community structure. Microbial communities were sampled from sediments in the Athabasca River and its tributaries as well as in oil sands tailings ponds. Bacterial and archaeal 16S rRNA genes were amplified and sequenced using next-generation sequencing technology (454 and Ion Torrent). Sediments were also analyzed for a variety of chemical and physical characteristics. Microbial communities in the fine tailings of the tailings ponds were strikingly distinct from those in the Athabasca River and tributary sediments. Microbial communities in sediments taken close to tailings ponds were more similar to those in the fine tailings of the tailings ponds than to the ones from sediments further away. Additionally, bacterial diversity was significantly lower in tailings pond sediments. Several taxonomic groups of Bacteria and Archaea showed significant correlations with the concentrations of different contaminants, highlighting their potential as bioindicators. We also extensively validated Ion Torrent sequencing in the context of environmental studies by comparing Ion Torrent and 454 data sets and by analyzing control samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it