Integrated estimation of the spatial population density surface using semi‐continuous sampling data
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
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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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 | 0.002 | 0.003 |
| 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