Pangenomic analysis of three putative hydrocarbon degrading genera <i>Limnohabitans, Aquabacterium</i>, and <i>Novosphingobium</i> collected from freshwater sources
Bibliographic record
Abstract
A pangenome analysis offers a unique exploration of the metabolic and genetic diversity, range of ecological niches, and evolution of a particular genus or species. However, such pangenomic analyses are uncommon among environmentally relevant genera. Here, we present freshwater pangenomes of three environmentally relevant genera, Limnohabitans, Aquabacterium, and Novosphingobium. These genera had been detected in hydrocarbon degrading cultures in previous research by our group. Using pangenomic tools we attempted to characterize the extent of hydrocarbon degradation potential within each pangenome and determine what ecological niche each genus occupies within hydrocarbon degradation. In total 46 Limnohabitans, 10 Aquabacterium, and 32 Novosphingobium freshwater genomes were collected from various databases and compiled into pangenomes. We found that each pangenome harbours downstream hydrocarbon degrading potential and unexpected genetic diversity within its core and accessory pangenomes possibly stemming from geographic and metagenomic data processing influences. This work was the first to explore pangenomes of these environmentally relevant genera.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".