Broken Eggs Influence on Fertilization Capacity and Viability of Eggs, Turbidity and pH of Ovarian Fluid and Fertilization Water in the Endangered Caspian Brown Trout, Salmo Trutta Caspius
Bibliographic record
Abstract
To identify a simple tool for quick evaluation of quality of endangered Caspian brown trout, Salmo trutta caspiuseggs, the changes of pH (both in ovarian fluid and fertilization water), turbidity (both in ovarian fluid andfertilization water), fertilization and eyeing rates were investigated in ovarian fluid samples containing perfecteggs as well as different concentrations of broken eggs. The pH of ovarian fluid and water as well as fertilizationand eyeing rates decreased significantly (P<0.05) with increasing of broken eggs in ovarian fluid. In contrast, theturbidity of ovarian fluid and water as well as mortality rate of eggs increased significantly (P<0.05) withincreasing of broken eggs in ovarian fluid. Also, significant correlations (P<0.01) were found between measuredparameters as follow: pH of ovarian fluid vs. pH of water and fertilization and eyeing rates; turbidity of ovarianfluid vs. turbidity of water; turbidity of ovarian fluid and water vs. fertilization and eyeing rates. Our resultsconclude that pH and turbidity of ovarian fluid and water effectively influence on efficiency of artificialpropagation. Therefore, these could be used as two simple tools for quick evaluation of quality of Caspian browntrout eggs during artificial reproduction in the hatchery.
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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 | 0.001 |
| 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".