Evaluating sampling efficiency in depletion surveys using hierarchical Bayes
Bibliographic record
Abstract
Estimating animal abundance is essential to natural resource management and conservation. However, the cost associated with abundance estimation can be high for populations that are difficult to sample. Researchers, particularly in fisheries management, often sample such populations using depletion or removal surveys. Depletion surveys rely upon successive removals of animals, without replacement, to estimate abundance. These researchers also must decide on other sampling protocol, including the depletion technique, which may include depletion gear-type, vessel, or personnel. To inform this decision, we propose a supplement to the hierarchical Bayesian models recently introduced for the analysis of depletion data. Using Bayesian sample size methodology along with hierarchical modeling, we present a method for estimating the efficiency of previously employed depletion techniques. Using this method, the researcher can estimate the expected variability in abundance estimates for each depletion technique and apply this information to future decisions. Additionally, this method allows the estimation of expected variability for various numbers of depletion passes. We demonstrate the methodology using a data set of Chesapeake Bay blue crab ( Callinectes sapidus ) depletion surveys.
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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 | 0.115 | 0.288 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".