Properties of boiled or steamed Procambarus clarkii girard and Procambarus zonangulus crawfish tail meat during refrigerated and frozen storage
Bibliographic record
Abstract
This research was conducted to address the different cooking methods of crawfish processors and determine the refrigerated or frozen shelf-life of cooked product. Live crawfish, (Procambarus clarkii girard and Procambarus zonangulus), were either boiled or steamed before storage of 11 days in refrigerated (3°C) conditions or six months in frozen storage (-18°C). There were minimal moisture, ash, protein, and fat differences with cooking type or storage type. There were no E.coli/coliforms in samples and aerobic plate counts were less than 3 log10 colony forming untis (CFU)/g after 6 mo frozen storage and higher than 3 log10 CFU/g after 3 days of refrigerated storage. Lipid oxidation by TBARS increased, but was less than 0.53 mg MDA/kg during storage. Peak force, total shear work, pH, L*, a*, b* color values were variable during storage, but not different between cooking treatments at each storage period. Mineral and fatty acid analyses were similarly variable. There were no differences between boiling and cooking crawfish for most variables and natural variation among the samples might explain variability in refrigerated and frozen storage. Processors can use either boiling or steaming to cook crawfish and store cooked crawfish for 3 days in refrigerated storage and for 6 weeks in frozen storage with minimal changes in properties.
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".