Erythromycin and florfenicol treatment of rainbow trout <i>Oncorhynchus mykiss</i> (Walbaum) experimentally infected with <i>Flavobacterium psychrophilum</i>
Bibliographic record
Abstract
Abstract Flavobacterium psychrophilum is responsible for significant economic losses in rainbow trout aquaculture. Antimicrobial treatment remains the primary means of control; however, there are limited choices available for use. The objectives of the study were therefore to determine the minimum inhibitory concentrations for erythromycin and florfenicol in selected F. psychrophilum isolates and to evaluate their clinical treatment efficacy in experimentally infected rainbow trout. All isolates tested had moderate susceptibility to florfenicol and erythromycin except one isolate, which had low susceptibility to erythromycin. Two isolates (one with moderate and one with low susceptibility to erythromycin) were used in an experimental infection trial. Rainbow trout juveniles were injected intraperitoneally with 108 cfu/fish and after mortality had begun, fish were given erythromycin‐ and florfenicol‐medicated feed at a rate of 75 mg kg−1 day−1 and 10 mg kg−1 day−1 fish body weight, respectively, for 10 consecutive days. The splenic F. psychrophilum load was determined using an rpoC quantitative PCR throughout the 30‐day trial. Relative to antibiotic‐free controls, erythromycin treatment significantly (p < 0.05) reduced mortality of rainbow trout juveniles infected with FPG101, even when treatment was initiated after clinical signs developed.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".