Estimating riverine fish population size from single- and multiple-pass removal sampling using a hierarchical model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A hierarchical model is described for estimating population size from single- and multiple-pass removal sampling. The model is appropriate for two-stage sampling schemes, typified by surveys of riverine fish populations, in which multiple sites are surveyed, but a low number of passes are undertaken at each site. The model estimates the average population size within the target area from the raw catch data, and thus allows for differences in the sampling procedure at each site, such as including single-pass sampling. The model also uses the data from all sites to estimate the population size at each individual site. This results in generally improved precision for multiple-pass sites and provides comparable estimates from single-pass sites. A Bayesian approach is described for estimating the parameters of the hierarchical model using sampling importance resampling (SIR). An empirical Bayesian approach, which ignores prior uncertainty but is simpler to implement, is also described. Application of the hierarchical model is illustrated with electrofishing data for 0+ trout (Salmo trutta) in the River Inny, U.K.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it