Recovering cellular biomass from fluids using chemical flocculation
Bibliographic record
Abstract
Summary We developed an efficient, scalable and inexpensive method for recovering cellular biomass from complex fluid matrices that cannot be processed using conventional filtration methods. The method uses chemical flocculation with iron oxyhydroxides, is capable of recovering greater than 90% of cellular biomass from fluids with more than 10 3 cells ml −1 , and was validated using both mock communities and field samples. High quality DNA can be readily extracted from iron flocs using standard soil extraction kits. We applied chemical flocculation to fracing fluids from British Columbia, Canada and recovered a diversity of microbial taxa including abundant members of the Epsilon‐ and Deltaproteobacteria previously recovered from shale gas operations in the United States. Application of chemical flocculation presents new opportunities for scalable time‐series monitoring and experimentation on complex fluid matrices including microbial community profiling and shotgun metagenomics over gas production well completion cycles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".