Effect of Ice Storage on the Chemical Composition and Lipid Quality in Fat Greenling (<i>Hexagrammos otakii</i>) and Black Rockfish (<i>Sebastes schlegelii</i>)
Bibliographic record
Abstract
The effect of ice storage on the chemical composition of fat greenling and black rockfish, especially their lipid quality were evaluated. Both physical and biochemical changes occurred during ice storage, resulting in increased moisture content, free fatty acid (FFA), acid values (AV), peroxide values (POV), thiobarbituric acid values (TBARS), and increased activities of lipases and lipoxygenase. Decreases in the ash, protein, phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), triacylglycerol (TAG), and fatty acid contents were also observed. The decreased PC, PE, and TAG values, together with the increased amount of AV and each FFA as well as lipases activity indicated the hydrolysis of lipids induced by lipases. In addition, the increased POV and TBARS as well as lipoxygenase activities, together with the decrease in the fatty acid contents, point to the lipoxygenase-assisted oxidation of lipids. Based on the increase rate of the total volatile basic nitrogen and FFA as well as the decreased rate of the PC, PE, TAG, saturated, monounsaturated, and polyunsaturated fatty acids during ice storage, black rockfish was more prone to spoilage, less sensitive to hydrolysis, and more sensitive to oxidation compared to fat greenling.
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 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".