Microbial Populations of the River-Recharged Fredericton Aquifer
Bibliographic record
Abstract
The City of Fredericton, New Brunswick, Canada obtains its water from the Saint John River-recharged Fredericton aquifer. Water quality improves as it flows from the river into the aquifer in a process called bank filtration. Microorganisms contribute to water quality improvements during bank filtration by removing organic carbon. In the Fredericton aquifer, microbially catalyzed reductive dissolution of manganese oxide minerals negatively affects water quality. Aerobic and anaerobic microorganisms were cultured from Fredericton aquifer production well water, from associated bedrock groundwater, and from Saint John River sediment core and sediment groundwater samples. Aerobes were the largest culturable groups in all samples. The ratio of aerobes to other microbial groups, i.e., those reducing nitrate, Mn4 +, Fe3 +, or sulfate, did not vary significantly along the water flow path from the river to the aquifer. Analysis of microbial community composition along the flow path indicated an essentially identical community except in the immediate vicinity of the aquifer. This is in agreement with the absence of macroscale redox zones in the sediment below the Saint John River as determined by groundwater geochemical data. Bacteria isolated from groundwater samples, identified by 16S ribosomal RNA gene sequencing, were α -, β -, γ -, and δ -Proteobacteria, Actinobacteria, and Firmicutes. In contrast to groundwater samples, the majority of bacteria isolated from sediments were γ -Proteobacteria. Numbers of manganese-reducing bacteria, including Aeromonas spp., were small, however Mn4 + reduction ability was widespread in bacteria enriched and isolated with other electron acceptors. The diverse Fredericton aquifer microbial community likely uses manganese oxide minerals as a sink for electrons derived from organic carbon oxidation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".