Comparing off‐flavor trends in freshwater recirculating aquaculture systems with microbially mature or immature biofilters while growing Atlantic salmon <scp> <i>Salmo salar</i> </scp>
Bibliographic record
Abstract
Abstract Geosmin (GSM) and 2‐methylisoborneol (MIB) cause objectionable off‐flavors in fish produced in recirculating aquaculture systems (RAS). Remediation solutions have not been developed; therefore, a study was conducted to determine if microbial maturity limits off‐flavor production. Triplicate RAS with newly established nitrification (“immature”) were compared to “mature” RAS operated continuously for 2.5 years, while growing market‐size Atlantic salmon, Salmo salar . Mean waterborne GSM and MIB levels peaked at 35–40 ng/L in the immature RAS but were maintained at <13 ng/L in the mature RAS. Similar trends were reflected in salmon flesh. After 2 months, fillet GSM levels in the immature and mature RAS were 696 ± 31 and 247 ± 30 ng/kg ( p = 0.001) respectively, and MIB was consistently higher in salmon from the immature RAS. The abundance of off‐flavor‐producing organisms was not associated with off‐flavor trends, suggesting production was related to the RAS environment. Total ammonia nitrogen, nitrite‐nitrogen, total suspended solids, heterotrophic bacteria count, and true color were significantly higher in the immature RAS, and nitrifier abundance was generally lower and less stable. Of these, machine learning identified true color as the most important feature affecting GSM. Ultimately, the microbially mature RAS minimized off‐flavor in water and salmon flesh.
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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) | 0.000 | 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".