Global fish abundance estimation from regular sampling: the geostatistical transitive method
Bibliographic record
Abstract
This article deals with the estimation of fish biomass based on regular samplings. The geostatistical transitive method is a design-based spatially explicit method based on few and falsifiable assumptions concerning the sampling strategy. The falsifiability of a hypothesis corresponds to our capacity to control its adequacy to field data in practice. We first describe the basics of the method, mention the questions relative to the covariogram estimation, the units, and the projections of the coordinates, and explain how to fit the model to the experimental covariogram. We then apply the method to an ICES (International Council for the Exploration of the Sea) triennial mackerel egg survey, with regular sampling, and to a Moroccan octopus survey, with regular stratified sampling. To compare the present technique with existing methods, the number and the falsifiability of their respective hypotheses are considered in addition to the bias, the convergence, and the estimation variance. As is often the case, data are assumed to be synoptic, and we discuss two examples of spatiotemporal methods.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".