Hydrocarboniphaga effusa gen. nov., sp. nov., a novel member of the γ-Proteobacteria active in alkane and aromatic hydrocarbon degradation
Bibliographic record
Abstract
Novel alkane-degrading strains of bacteria were isolated from soil contaminated with fuel oil from a leaking underground tank in New Jersey, USA. Two phenotypically similar strains (designated AP102 and AP103T) possessed 16S rRNA sequences unique among the majority of known hydrocarbon-degrading bacteria. The 16S rRNA sequences showed a moderate but distant relationship to the genus Nevskia and a substantial similarity to strains that had previously been isolated for growth on phenol (in Japan) and on toluene (in Canada) by other researchers. The hydrocarbon-degrading strains from Japan, Canada and New Jersey showed no resemblance to the typical morphology of Nevskia but did share a striking similarity among themselves in cell morphology, in the unusual appearance of colonies on various solid media and in various physiological properties. A full taxonomic analysis was performed, including DNA-DNA hybridization and nutritional screening with 117 organic compounds as sole sources of carbon and energy. The strains are active in the degradation of important environmental pollutants, and their phenotypic, physiological, metabolic and genomic properties suggest that they are members of a novel taxon in the gamma-Proteobacteria, for which the name Hydrocarboniphaga gen. nov. is proposed, with the single species Hydrocarboniphaga effusa sp. nov. The type strain is AP103T (=ATCC BAA-332T=DSM 16095T).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".