Microbial control during the incubation of rainbow trout (Oncorhynchus mykiss) eggs exposed to humic acid
Bibliographic record
Abstract
Introduction Cultured fish species are increasingly exposed to fungi and bacteria in the rearing environment, often causing disease and mortality causing aquaculture producers to rely on the use of chemical therapeutants which may have negative consequences for both human and environmental health. This study investigated the effect of humic acid as a treatment to reduce fungal and bacterial infections to increase survival during the incubation of rainbow trout (Oncorhynchus mykiss) eggs. Methods Humic acid, an aqueous extract of oxidized lignite was added to water entering both stacked tray and jar type incubators at a dosage rate of 5 mg/L from fertilization until hatching. Results The water treatment eliminated observable fungus and significantly improved survival until hatching in the treated incubator trays (77.2%) compared to the untreated controls (55.5%) (p ≤ 0.001). Egg survival was not improved in the incubator jars. The humic acid reduced bacterial diversity, but only in the stacked tray incubators and altered bacterial composition of the water after 20 days of treatment. Discussion The treatment increased the bacterial abundance of Burkholderiales, positively associated with healthy fish eggs and decreased the abundance of Flavobacterium and Aeromonas, known fish pathogens. These findings support the topical treatment of humic acid as a potential alternative treatment to prevent fungal infections and reduce mortality during incubation of rainbow trout eggs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".