Aquifer environment selects for microbial species cohorts in sediment and groundwater
Bibliographic record
Abstract
Little is known about the biogeography or stability of sediment-associated microbial community membership because these environments are biologically complex and generally difficult to sample. High-throughput-sequencing methods provide new opportunities to simultaneously genomically sample and track microbial community members across a large number of sampling sites or times, with higher taxonomic resolution than is associated with 16 S ribosomal RNA gene surveys, and without the disadvantages of primer bias and gene copy number uncertainty. We characterized a sediment community at 5 m depth in an aquifer adjacent to the Colorado River and tracked its most abundant 133 organisms across 36 different sediment and groundwater samples. We sampled sites separated by centimeters, meters and tens of meters, collected on seven occasions over 6 years. Analysis of 1.4 terabase pairs of DNA sequence showed that these 133 organisms were more consistently detected in saturated sediments than in samples from the vadose zone, from distant locations or from groundwater filtrates. Abundance profiles across aquifer locations and from different sampling times identified organism cohorts that comprised subsets of the 133 organisms that were consistently associated. The data suggest that cohorts are partly selected for by shared environmental adaptation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".