Development of a sandwich hybridization assay for the identification and quantification of red drum (<i>Sciaenops ocellatus</i>) eggs: a novel tool for fishery research and management
Bibliographic record
Abstract
Egg identification and quantification are crucial to understanding the spawning and recruitment dynamics of economically important fish species. This study describes the development of a novel molecular method for finfish egg identification that eliminates the need for time-consuming microscopy. Sandwich hybridization assay (SHA) uses two ribosomal RNA (rRNA)-targeted oligonucleotides to directly detect unpurified and unamplified rRNA. Probes were designed to complement the internal transcribed spacer (ITS) region of the red drum (Sciaenops ocellatus), an important recreational game fish for which spawning and reproductive information is sparse. Sample homogenization procedures were modified to disrupt egg chorion, and the resultant assay detected S. ocellatus eggs and tissues without cross-reactivity. Standard curves were linear (y450 = 0.001x + 0.054; R2 = 0.999), showing potential for quantitative uses, and the lower limit of detection was 5 eggs·mL−1 homogenate. Ontogenetic stage had a significant effect (ANOVA, p < 0.05) on optical density. The assay successfully detected S.ocellatus eggs in field samples from Charleston Harbor, South Carolina, and could be incorporated into current management practices or adapted to other species.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".