HIERARCHICAL MODELS IMPROVE ABUNDANCE ESTIMATES: SPAWNING BIOMASS OF HOKI IN COOK STRAIT, NEW ZEALAND
Bibliographic record
Abstract
It is often difficult to estimate abundance for a dynamic population, i.e., one that is moving through the survey area or in which birth or mortality rates are high. One approach is to estimate the proportion of animals present during each survey, using a model that estimates the dynamics of the survey proportion of the population. However, this can increase the uncertainty of the estimates if the dynamics parameters are not well estimated. Here we approached this problem by developing methods using hierarchical model structures, which allow us to share information on the dynamics parameters across years. We applied this modeling approach to the estimation of residence time and spawning biomass for New Zealand hoki ( Macruronus novaezelandiae ) in Cook Strait spawning grounds. By sharing parameters across years, we obtained better parameter estimates than by the traditional assumption that the dynamics in one year are independent of those of other years. By integrating the estimation of residence time into a dynamic model using simulated maximum likelihood methods, we also were able to calibrate acoustic estimates of spawning biomass for the fact that not all individuals are on the grounds at the time of the acoustic survey. We discuss alternative model formulations for the application of hierarchical methods to stage‐structured data and the analysis of data from acoustic surveys of spawning fish.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".