Bayesian posterior prediction of the patchy spatial distributions of small pelagic fish in regions of suitable habitat
Bibliographic record
Abstract
Small pelagic fish aggregate within areas of suitable habitat to form patchy distributions with localized peaks in abundance. This presents challenges for geostatistical methods designed to investigate the processes underpinning the spatial distribution of stocks and simulate distributions for further analysis. In two-stage models, presence–absence is treated as separable and independent from the process explaining nonzero densities. This is appropriate where gaps in the distribution are attributable to one process and conditional abundance to another, but less so where patchiness is attributable primarily to the strong schooling tendencies of small pelagic fish within suitable habitat. We therefore developed a new modelling framework based on a truncated Gaussian random field (GRF) within a Bayesian framework. We evaluated this method using simulated test data and then applied it to acoustic survey data for Peruvian anchoveta (Engraulis ringens). We assessed the method’s performance in terms of posterior densities of spatial parameters, and the density distribution, spatial pattern, and overall spatial distribution of posterior predictions. We conclude that Bayesian posterior prediction based on a truncated GRF is effective at reproducing the patchiness of the observed spatial distribution of anchoveta.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".