Towed passive acoustic monitoring complements visual survey methods for Heaviside’s dolphins <i>Cephalorhynchus heavisidii</i> in the Namibian Islands Marine Protected Area
Bibliographic record
Abstract
The genus Cephalorhynchus contains four dolphin species, of which three are classified as Near Threatened or Endangered and one subspecies is close to extinction. Understanding the species’ abundance, distributions and habitat preferences is necessary for effective management to prevent further population declines. Heaviside’s dolphin C. heavisidii is endemic to the Benguela ecosystem off southwest Africa, and like other Cephalorhynchus species these dolphins produce narrowband high-frequency (NBHF) echolocation clicks with a centroid frequency around 125 kHz. We conducted dedicated visual and acoustic line-transect surveys within and adjacent to the Namibian Islands Marine Protected Area in 2012–2014. Acoustic data were processed in the passive acoustic monitoring software PAMGuard, using the default porpoise click detector and classifier to identify NBHF echolocation clicks. Click detection and classification in PAMGuard included a large excess of false positives, which were easily identified by manual verification of events, and ultimately provided 52 definite detections. The acoustic methods provided data in offshore areas and during overnight periods, but were imperfect and not suitable for ecologically important shallow coastal areas. While demonstrating the utility of passive acoustic monitoring in line-transect surveys targeting Cephalorhynchus species, the study shows that both visual and acoustic methods were needed to collect data throughout the range of Heaviside’s dolphin.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".