Genetic identification of eleven aquatic bacteria using the 16S rDNA gene
Bibliographic record
Abstract
Bacteria help maintain ecological balance by participating in the carbon, oxygen, and/or nitrogen cycles. These cycles are important for the organism’s survival, that’s why their identification is fundamental in order to determine how they function and interact in an ecosystem. In this study, eleven previously isolated bacteria from the upstate New York waterbodies (the Oswego River, Lake Ontario, and Lake Neatahwanta) were identified by sequencing a fragment of the 16S rDNA gene from each bacteria. Identification was supported by metabolic tests. The sequences were compared in the nucleotide database in GenBank (GenBank, 2009) and then aligned to construct a phylogenetic tree. The eleven bacteria were grouped into eight genera: Acinetobacter, Aeromonas, Planococcus, Enterobacter, Exiguobacterium, Pseudomonas, Plesiomonas, and Staphylococcus. The metabolic tests better supported the identification for the samples with longer sequences than samples with shorter sequences, which shows the importance of getting longer sequences to better identify bacteria. The identified genera of the isolated bacteria were found to occur in aquatic environments and Plesiomona shigelloides, one of the samples, is usually found in fish and other aquatic animals. Genetic identification of bacteria 3
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".