A diver survey design to estimate absolute density, biomass, and spatial distribution of abalone
Bibliographic record
Abstract
Abalone surveys worldwide measure relative stock abundance. However, important advantages accrue if diver surveys measure absolute numbers or biomass per square metre. Principally, absolute biomass permits quota setting from a single survey using a decision table. Although relative abundance surveys have permanently fixed sampling protocols and locations, absolute abundance survey designs can be improved with technology over time. Furthermore, surveys can be directed to areas of principal management focus, and absolute survey population numbers by length with confidence intervals provide informative model input. We propose and test a transect survey design to estimate and map absolute density and biomass of abalone or other sedentary invertebrates. Divers count and measure all abalone within 1 m of a 100 m, boat-deployed leaded rope line. Semi-systematic transect locations provide spatially representative sampling inside bounded survey regions and geostatistical data for contour maps of abalone density and mean size. The effectiveness of the design for estimating change in population size under harvesting and for locating areas of fishable density was tested by a fish-down experiment, using surveys run before and after commercial harvest. The leaded-line survey design estimates of population change and spatial distribution showed agreement with the fish-down experimental harvest.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".