Bacterial bioclusters relate to hydrochemistry in New Zealand groundwater
Bibliographic record
Abstract
Groundwater is a major source of New Zealand's water supply and supports base flows in rivers. Microbial communities in groundwater ecosystems mediate biogeochemical processes, and it is therefore crucial to understand microbial diversity in these ecosystems. We analysed bacterial assemblages from 35 New Zealand groundwater monitoring sites with varying hydrogeochemical conditions across the country. Proteobacteria was the most abundant phylum, and Variovorax represented the most common taxon. Pseudomonas, Burkholderia, Acidovorax, Janthinobacterium, Polaromonas and Caulobacter were the other common taxa. There was no Operational Taxonomic Unit (OTU) that was found in every one of the 35 samples. Here, we introduce a framework that has potential utility for groundwater ecosystem management, where the samples with similar microbial communities are grouped together into 'bioclusters'. Metabolic inferences derived from the taxonomic data were used to predict the oxygen requirements, metabolic potential and bacterial energy sources of each biocluster. Groundwater chemistry explains 59% of the variation in the relative abundance of all OTUs, with NO3-N, pH, DO, NH4-N, Fe, Br and SO4 displaying the strongest relationships to bioclusters. We propose that the biocluster framework, coupled with metabolic inferences derived from the taxonomic data, may have application outside New Zealand for on-going monitoring of the health of groundwater ecosystems.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".