Fisheries and aquaculture related biometrics of the sea cucumber Cucumaria frondosa: tagging, resistance to stress and influence of diet on lipid composition
Bibliographic record
Abstract
Cucumaria frondosa is widely distributed in the North Atlantic where it has been increasingly exploited to supplement the growing demand for sea cucumber products in Asian markets. The objectives of this study were based on knowledge gaps identified by the stakeholders of the sea cucumber industry in eastern Canada. The first study investigated marking techniques using passive integrated transponder (PIT) tags to facilitate sea cucumber research. The second study identified the most suitable media for refrigeration during live storage and transport to address concerns with skin and meat integrity prior to processing. The third study focused on principles of aquaculture by examining growth, and lipid class and fatty acid profiles of muscle and gonad tissues of sea cucumbers fed with either diatoms or fish eggs. Implanting PIT tags at the base of the tentacles to reach the aquapharyngeal bulb emerged as one of the most effective techniques ever developed for tagging sea cucumbers reliably and innocuously for long periods. The most suitable transport media for live boreal/temperate sea cucumbers was determined to be iced seawater (cold seawater with freshwater ice). Finally, while sea cucumbers were able to feed on live diatoms (Chaetoceros muelleri) as well as commercial fish eggs, the latter diet yielded greater body length increment, specific growth rate and ratio of essential DHA:EPA in gonadal tissues. In contrast, sea cucumbers fed with diatoms exhibited the highest ratio of the essential fatty acids ARA to EPA in muscle tissues. The findings presented here will hopefully assist ecological and conservation studies and the sustainable development of sea cucumber fisheries and aquaculture programs worldwide.
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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".