Length and Weight Reduction in Larval and Juvenile Yellow Perch Preserved with Dry Ice, Formalin, and Ethanol
Bibliographic record
Abstract
Abstract Due to the increasing interest in biochemical indices such as the RNA–DNA ratio used to measure fish growth, fish often need to be stored frozen with dry ice (i.e., −80°C). The objectives of this study were to (1) quantify the effects of dry ice on both the length and weight of larval and juvenile yellow perch Perca flavescens preserved for storage periods of 15 d and 7–8 months, (2) compare these effects with those of two commonly used preservatives (a 10% solution of formalin and a 75% solution of ethanol), and (3) provide equations to convert the lengths and weights of larval and juvenile yellow perch preserved with dry ice, formalin, and ethanol back to their initial unpreserved values. For all preservation methods, fish weight was more affected than length. The smallest length reduction was observed with formalin (short term: 2.1% and 0.1% for larvae and juveniles, respectively; long term: 10.1% and 1.2%), followed by dry ice (short term: 4.0% and 1.4%; long term: 7.2% and 3.9%) and ethanol (short term: 9.6% and 1.2%; long term: 11.7% and 1.2%). The smallest weight reduction was also observed with formalin (short term: 21.9% and 2.2%; long term: 23.2% and 3.9%), followed by dry ice (short term: 54.0% and 11.1%; long term: 52.8% and 8.4%) and ethanol (short term: 61.1% and 22.0%; long term: 66.0% and 26.0%). Except for one case, all of the regression equations that were built to convert the lengths and weights of larval and juvenile yellow perch preserved with dry ice, formalin, and ethanol back to initial measurements were highly significant.
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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 | 0.001 | 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".