Integrated estimation of the spatial population density surface using semi‐continuous sampling data
Bibliographic record
Abstract
Abstract Mixture models are frequently used in ecology for the estimation of abundance. These models adopt a hierarchical structure in which the observations are dependent on both a detection probability and abundance at the survey site. Applications are typically to discrete survey count data. Analogous mixture models have not been developed for semi‐continuous sampling data, which are characterised by a large number of zero observations and non‐zero observations measured on a continuous scale. We attempt to bridge the gap between mixture modelling approaches developed for discrete counts and their application to semi‐continuous data. We use survival analysis to derive a relationship between a continuous measure of abundance and the probability of a zero observation, and incorporate this relationship into a two‐part, log‐normal hurdle model, with the biomass represented as a hierarchical model parameter. We apply the model to semi‐continuous marine sampling data collected from a bottom trawl fishery in New Zealand. Despite the simplicity of the parameterisation, the model is able to describe the observations and predict a relative biomass density layer over space. The approach allows mixture models to be applied to semi‐continuous ecological data. By allowing the population density distribution to be properly estimated, the methods presented here can inform the management of anthropogenic impacts on vulnerable species, as well as understanding distributional shifts that may arise due to climate change.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".