Evaluation of Sampling Designs for Red Sea Urchins Strongylocentrotus franciscanus in British Columbia
Bibliographic record
Abstract
Abstract Estimates of the total stock biomass of marine invertebrates that aggregate, such as red sea urchins Strongylocentrotus franciscanus, are often highly uncertain, partly because it is difficult to estimate their density. To improve estimates, we used 200 simulated red sea urchin populations with spatial and numerical properties based on field data to evaluate various simulated survey designs for a given number of transects in terms of the precision, bias, and efficiency (relative variance) of their estimates. We considered a random transect sampling method that is currently used in British Columbia for red sea urchins, which samples every other quadrat within each transect, as well as a complete version of that transect method, which samples every quadrat. We also evaluated more complex random transect sampling designs, including restricted adaptive cluster sampling and a design stratified by type of substrate within each transect. The complete transect method produced essentially unbiased estimates of red sea urchin density (as did the currently used sampling design) and had lower variance than the current method, but the complete method used twice as many quadrat samples per transect to do so (incurring higher costs of sampling by divers). In contrast, the design stratified by substrate required 33% fewer sampled quadrats per transect than the current sampling method to achieve the same variance as that method, but it had a median bias of 10%. Finally, the restricted adaptive cluster sampling design gave estimates that had lower variance than the current method and used 18% fewer sampled quadrats, but the median urchin density estimate was biased downward by 8%. Choosing among sampling designs thus involves making trade-offs among bias, precision, and sampling cost as well as considering practical constraints on scuba divers who attempt to implement complex designs in field situations.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".