Handling and Storage Procedures Have Variable Effects on Fatty Acid Content in Fishes with Different Lipid Quantities
Bibliographic record
Abstract
It is commonly assumed that the most accurate data on fatty acid (FA) contents are obtained when samples are analyzed immediately after collection. For logistical reasons, however, this is not always feasible and samples are often kept on ice or frozen at various temperatures and for diverse time periods. We quantified temporal changes of selected FA (μg FAME per mg tissue dry weight) from 6 fish species subjected to 2 handling and 3 storage methods and compared them to FA contents from muscle tissue samples that were processed immediately. The following species were investigated: Common Carp (Cyprinus carpio), Freshwater Drum (Aplodinotus grunniens), Channel Catfish (Ictalurus punctatus), Antarctic Eelpout (Pachycara brachycephalum), Rainbow Trout (Oncorhynchus mykiss) and Arctic Charr (Salvelinus alpinus). The impact of storage method and duration of storage on FA contents were species-specific, but not FA-specific. There was no advantage in using nitrogen gas for tissue samples held on ice for 1 week; however, holding tissue samples on ice for 1 week resulted in a loss of FA in Charr. In addition, most FA in Trout and Charr decreased in quantity after being stored between 3 and 6 hours on ice. Freezer storage temperature (-80 or -20°C) also had a significant effect on FA contents in some species. Generally, we recommend that species with high total lipid content (e.g. Charr and Trout) should be treated with extra caution to avoid changes in FA contents, with time on ice and time spent in a freezer emerging as significant factors that changed FA contents.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".