Bacterial changes and quality deterioration of freshwater shellfish <i>Hyriopsis cumingii</i> meat under different temperature storage
Bibliographic record
Abstract
Hyriopsis cumingii is an important economic freshwater shellfish in China and there is a need to understand changes in the microbial community structure resulting in multidimensional quality degradation when the fish is stored at different temperatures. This study integrated 16S rRNA full-length sequencing with multidimensional quality indicators to investigate the temperature-regulated bacterial community shifts and quality deterioration mechanisms in stored H. cumingii meat. The results showed that bacterial richness (Chao1 index) decreased progressively with both refrigerated (4 °C) and room-temperature (25 °C) storage. Community composition underwent significant restructuring, with Bacteroidota decreasing at 25 °C while Bacillota increased compared to 4 °C storage. Additionally, the refrigerated group showed enrichment of Delftia turuhatensis and Chryseobacterium indologenes compared to the room-temperature storage group. Temperature significantly restructured bacterial communities, with notably higher pathogenic bacteria under refrigeration and spoilage bacteria dominance at room temperature. Metagenomic functional profiling revealed temperature-driven metabolic pathway divergence, indicating distinct spoilage mechanism. Predictable quality changes in H. cumingii correlated with temperature-imposed microbial composition.
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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) | 0.001 | 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".