Improving Outplanting Designs for Northern Abalone (<i>Haliotis</i><i>kamtschatkana</i>): The Addition of Complex Substrate Increases Survival
Bibliographic record
Abstract
Hatchery-reared abalone outplanted into the wild at higher than ambient densities often experience very high levels of mortality as a result of density-dependent predation by natural predators. We conducted 2 experiments to assess the effectiveness of different methods of reducing predation on outplanted northern abalone (Haliotis kamtschatkana). During the first experiment, we removed small predators from brick-filled habitat cages that excluded large predators. The survival rates of juvenile abalone (mean shell length (SL), 22 mm) were greater in cages from which sea stars and other predators were removed periodically than in control cages. During the second experiment, we constructed 30 fenced 1 -m2 outplanting plots and assigned 10 randomly to be controls and 20 to be filled with 1 of 4 complex substrates (0.3-m layer of cobbles, 0.3-m layer of boulders, 0.9-m layer of cobbles, and 0.9-m layer of boulders). We then released 30 large juvenile abalone (mean SL, 51.5 mm) into each plot. The number of live outplanted abalone remaining in the complex substrate plots was significantly greater than in the control plots during the first 6 days. Surveys of a 4-m radius around each plot showed that emigration was lower from complex substrate plots than from control plots, and fewer shell fragments were found. The addition of complex substrate to plots provided crypsis from large predators but also resulted in significantly higher densities of small predators. Outplanting lower densities of abalone into larger plots of natural high-substrate complexity might attract fewer predatory sea stars and crabs, and thus result in higher survival rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".