A geostatistical method for assessing biomassof tuna aggregations around moored fish aggregating devices with star acoustic surveys
Bibliographic record
Abstract
Universal kriging was used to model the spatio-temporal variability in the acoustic density of tuna aggregations recorded during star echosounding surveys around moored fish aggregating devices (FADs) in Martinique (Lesser Antilles). The large-scale deterministic drift in the tuna spatial distribution was modeled using an advection–diffusion equation applied to animal grouping. Residuals from the drift were modeled as a random component with small-scale spatial correlation. An estimation variance formula was derived from this deterministic–statistical mixed model to assess the mean precision of density estimates of daytime tuna aggregation. The mean relative error obtained with our star design for daytime surveys was 24%. The methodology was applied to estimate daily maxima of tuna biomass around moored FADs during four monthly sea cruises. The daily peak of tuna biomass aggregated around moored FADs was 9 t on average (standard deviation = 4). Estimation variances for different survey designs were compared for optimizing sampling strategy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".