A spatial model to estimate gear efficiency and animal density from depletion experiments
Bibliographic record
Abstract
Depletion experiments are conducted to estimate efficiency of sampling gear and density of organisms. Traditional models for analyzing these experiments make restrictive assumptions that are often violated. We present a new spatial model, suitable for sessile benthic invertebrates, that does not depend on these restrictive assumptions. The new model (i) allows flexibility during the experiment in choosing the spatial location of successive samples, (ii) does not require organisms or successive samples to be randomized over the entire area of the experiment, and (iii) permits target organisms to be lost or added during the experiment. The model treats total catch per sample as a sum of catches from smaller cells with different, but known, sampling histories. A negative binomial model is used to describe the distribution of catches from tows made during the depletion experiment. Maximum likelihood methods are used to estimate parameters, derive confidence regions for parameters, and evaluate goodness of fit between data and the model. Data from an experiment involving Atlantic surfclams (Spisula solidissima) are used to demonstrate the model.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".