Effect of Transport Method on Subsequent Survivorship and Gonad Yield/Quality in the Red Sea Urchin <i>Mesocentrotus franciscanus</i>
Bibliographic record
Abstract
Abstract Sea urchin gonad enhancement entails collecting adults with low gonad yields from the wild, placing them in land-based or sea-based captivity, and feeding them a prepared or natural diet to produce high quality/quantity gonads for marketing. Collection and transportation to the culture facility can be stressful to the urchins and cause subsequent mortalities and suboptimal gonad yield/quality. Minimizing this handling stress is critical to ensuring maximum survivorship and optimal gonad enhancement, yet very little research has examined this, with no information being available for the red sea urchin Mesocentrotus franciscanus. The present study examined survivorship, gonad yield, and gonad quality of red sea urchins 2 weeks after exposure to three 2-h transport methods: (1) milk crates placed in two plastic fish totes filled with ambient seawater (“wet crate”), (2) sealed Styrofoam boxes filled with ambient seawater (“wet box”), and (3) sealed Styrofoam boxes without seawater, but with wet burlap placed on top of the urchins (“dry box”). While no gonadal parameters significantly differed between transportation methods, the percentage of survivorship for both the wet boxes and wet crates was 100%, while that for the dry boxes was 58.3% after 2 weeks, indicating that wet transport should be preferred over dry transport for red sea urchins.
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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".