Testing various geostatistical models to combine bottom trawl catches and acoustic data
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The aim of the CATEFA project is to combine information on demersal fish stock abundance from acoustic and bottom trawl surveys. While acoustic data are collected continuously while the research ship is underway, it is likely that combining two sources of information on the same variable should improve abundance estimation. A variety of geostatistical models are compared, contrasted and the output described. In this study, twenty scientific surveys from three areas (the North Sea, the Irish Sea and the Barents Sea) are analysed. These 3 zones have diverse species assemblages and hydrographic environments. Nevertheless we manage to find models relevant to most of these different situations. Unfortunately, however, we show that this enhancement can increase the variance of the estimation because of the inherently high variability of acoustic recordings. The purpose of this paper is to compare the results of three geostatistical models (i.e. co-kriging, model with orthogonal residuals, kriging with external drift) in terms of precision, details of maps, variance local and global of the estimation’s errors and cross validation. The role of the acoustic in each model and the precision it brings, is then discussed.
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 | 0.038 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".