Developing molecular tools to assess the biogeochemical/microbial community structure of oil sand processed waste material
Bibliographic record
Abstract
Microbial communities can dominate Fluid Fine Tailings (FFT) in the presence of electron acceptors (e.g. Sulfate). Sulfate reduction can produce hydrogen sulfide, one of several chemical constituents responsible for sediment oxygen demand (SOD). The preservation of RNA is a crucial step to study active microbial populations and their activity in FFT and hence understand the biological factors contributing to SOD. In our study different RNA preservation methods were tested to preserve microbial RNA in FFT sample. The results confirmed that LifeGuard(TM) Soil Preservation Solution (MO BIO Laboratories, Inc, California) is the best preservative method for RNA preservation. Through T-RFLP analysis of 16s rRNA and 16s rDNA, SRB's (Sulfate Reducing Bacteria) are shown to dominate the FFT during initial stages of incubation but its population decreased significantly over-time. This observation suggests that sulfate reduction is a self-limiting process and has less impact on the quality of overlying water column.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".