Hierarchical model to predict common carp and bigmouth buffalo abundance from electrofishing data
Bibliographic record
Abstract
Catch per unit effort (CPUE) is used as an index of fish abundance under the premise that changes in CPUE result from changes in true density. However, catchability may also vary based on environmental conditions that affect observed CPUE. We developed a hierarchical model for estimating common carp ( Cyprinus carpio) and bigmouth buffalo ( Ictiobus cyprinellus) relative abundance with electrofishing survey data from six shallow lakes in northwest Iowa, USA, between 2018 and 2020. Common carp catchability was negatively associated with lake perimeter but unrelated to lake surface area, water depth, Secchi depth, temperature, and month of sampling. Bigmouth buffalo catchability was negatively associated with Secchi depth and water temperature and unrelated to other environmental variables. Hierarchical model posterior distributions of bigmouth buffalo density were less precise than Schnabel estimates, whereas common carp posterior distribution abundance estimates were more precise than Schnabel estimates. Our results indicate that hierarchical models can be a viable substitute for labor-intensive capture–mark–recapture methods to estimate unknown latent variables like relative abundance, and could be applied to other species, sampling gears, and management frameworks.
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.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".