Indicator variables for a robust estimation of an acoustic index of abundance
Bibliographic record
Abstract
In North West Africa, pelagic fisheries are an essential economic sector. However, the scientific community fails to perform satisfactory assessments of key pelagic species like sardinella, owing to a lack of relevant indices of abundance to tune the model. This paper provides an alternative acoustic index based on a semiquantitative modelisation of acoustic densities. Acoustic energy is split into binomial variables coding for null, low, medium, large, and very large densities. A multivariate geostatistical approach allows (i) mapping the spatial distribution of classes of densities and (ii) computing a new acoustic index of abundance for Sardinella aurita and Sardinella maderensis. We used the surveys of RV Fridtjof Nansen (1995–2006) and RV Al-Awam (2007–2010). Our results indicated that empirical spatial structures were highly stable over time for both between areas and surveys. Co-kriging maps also showed that sardinella had stable hot spots of distribution. The indices of abundance developed in the present study were tested in an assessment procedure and outperformed all the indices used routinely by the FAO–CECAF (Fishery Committee for the Eastern Central Atlantic) assessment working group.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".