Improved Growth Media for Isolation and Identification of Fish Pathogenic Tenacibaculum spp.
Bibliographic record
Abstract
Standard blood agar medium with 2% NaCl (BAS) and Marine Agar (MA) are commonly used in bacteriological investigations of winter ulcers in farmed Atlantic salmon (Salmo salar Linnaeus) in Norway and allow easy recovery of Moritella viscosa based on its characteristic viscous colonies and β-hemolytic activity. However, the recent increase in cases of winter ulcers involving Tenacibaculum spp. and the potential emergence of T. maritimum due to rising temperatures highlight the need for improved methods of isolation and identification. Indeed, the recovery of Tenacibaculum spp. from outbreaks of winter ulcers or tenacibaculosis can be challenging. Despite the development of several agar media over the years to overcome this issue, such as Flexibacter maritimus medium (FMM), it remains difficult to differentiate Tenacibaculum species. We evaluated the growth dynamics and phenotypic characteristics of 13 bacterial isolates commonly associated with ulcer outbreaks on five different agar media, including two new formulations: kanamycin-supplemented marine blood agar for the selective isolation of Tenacibaculum spp. (KABAMA) and general blood agar for marine bacteria (BAMA). These new media facilitate the identification of Tenacibaculum spp., including T. maritimum, by distinguishing colonies based on their specific color, shape, and hemolytic activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".