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Record W4416563433 · doi:10.1111/jwas.70067

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>

2025· article· en· W4416563433 on OpenAlexaff
John Davidson, Curtis Crouse, Christine Lepine, Rakesh Ranjan, Julianna Stangroom, Jordan Poley, Christopher Good

Bibliographic record

VenueJournal of the World Aquaculture Society · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsUniversity of Prince Edward Island
FundersNational Institute of Food and AgricultureNational Oceanic and Atmospheric AdministrationU.S. Department of Agriculture
KeywordsRecirculating aquaculture systemAquacultureHeterotrophBiofilterAbundance (ecology)NitrificationGeosminFish farming

Abstract

fetched live from OpenAlex

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 &lt;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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.225
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the World Aquaculture SocietySame topicAquaculture Nutrition and GrowthFrench-language works237,207