Cod Larviculture Using High‐density Rotifer Production with Different Enrichments
Bibliographic record
Abstract
Abstract High‐density (HD) rotifer culture systems have been recently commercialized, but are not commonly used by the aquaculture industry. The aim of this study was to determine if HD systems could be used in cod hatcheries. An enrichment strategy using the commercial products, ArteMac and Protein Selco Plus (Com), was compared with the manufacturer's suggested enrichment, using Pavlova‐DHA (Pav). The Pav enrichment increased the eicosapentaenoic acid (EPA) levels in rotifers, but reduced the docosahexaenoic acid (DHA) and 22:5n‐6 (n‐6DPA, docosapentaenoic acid) levels. Larvae EPA levels in both polar and neutral lipids were relatively stable in the larvae fed with Com‐rotifers; while they were higher in early stages, they were progressively reduced through ontogeny in the Pav‐rotifers fed‐larvae. DHA levels in polar lipids decreased in larvae, particularly when fed with HD‐Pav rotifers. In all larvae, arachidonic acid (ARA) levels increased in the polar and neutral lipids, regardless of treatments. In both lipid fractions, the levels of ARA were quite stable in time, but still higher in larvae fed with Com‐rotifers. Bacterial load was lower in larvae fed with Pav‐rotifers. Denaturing gradient gel electrophoresis (DGGE) bacterial profiles of larvae and rotifers were all similar. This study shows the potential of using HD systems to produce rotifers, but highlights the necessity of adjusting the nutritional composition of rotifers prior to being fed to larvae.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".