Response of aquatic microbial communities and bioindicator modelling of hydraulic fracturing flowback and produced water
Bibliographic record
Abstract
The response of microbial communities to releases of hydraulic fracturing flowback and produced water (PW) may influence ecosystem functions. However, knowledge of the effects of PW spills on freshwater microbiota is limited. Here, we conducted two separate experiments: 16S rRNA gene sequencing combined with random forests modelling was used to assess freshwater community changes in simulated PW spills by volume from 0.05% to 50%. In a separate experiment, live/dead cell viability in a freshwater community was tested during exposure to 10% PW by volume. Three distinct patterns of microbial community shifts were identified: (i) indigenous freshwater genera remained dominant in <2.5% PW, (ii) from 2.5% to 5% PW, potential PW organic degraders such as Pseudomonas, Rheinheimera and Brevundimonas became dominant, and (iii) no significant change in the relative abundance of taxa was observed in >5% PW. Microbial taxa including less abundant genera such as Cellvibrio were potential bioindicators for the degree of contamination with PW. Additionally, live cells were quickly damaged by adding 10% PW, but cell counts recovered in the following days. Our study shows that the responses of freshwater microbiota vary by spill size, and these responses show promise as effective fingerprints for PW spills in aquatic environments.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".