Restoration aquaculture of the pinto abalone (Haliotis kamtschatkana kamtschatkana Jonas): impacts of rearing method on behaviour, growth and survivorship in the hatchery
Bibliographic record
Abstract
Pinto abalone (Haliotis kamtschatkana kamtschatkana) populations in Washington State (USA) and British Columbia (Canada) continue to decline despite fisheries closures. For successful recovery, supplementation may be necessary. To determine appropriate culture methods, juveniles were reared in habitat-enriched tanks (supplemented with rocks, macroalgae and sea urchins) or conventional aquaculture tanks and assessed for growth and survivorship in the laboratory over 15 months. No differences in survivorship or growth were observed. Subsequent experiments examined whether abalone behaviour (habitat selection and movement patterns) differed between rearing treatments. Abalone were exposed to one of three predator treatments (sea star arm, small crab, or no predator (control)) and filmed for 8 h. Abalone from habitat-enriched tanks changed habitats significantly more often than abalone from conventional tanks regardless of predator treatment. Significant differences in the percentage of time that abalone occupied the various habitats were also observed. Abalone in the sea star and control treatments primarily occupied the rocks, whereas abalone in the crab treatment behaved differently depending on the rearing method; conventionally reared abalone spent more time in corners, whereas abalone from habitat-enriched tanks spent more time exposed. These results demonstrate that rearing conditions can affect abalone behaviour and should be considered for abalone restoration efforts 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 | 0.000 | 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".