Dolphin communication during widespread systematic noise reduction-a natural experiment amid COVID-19 lockdowns
Bibliographic record
Abstract
Underwater noise from human activities is recognized as a world-wide problem, with important repercussions on the acoustic communication of aquatic mammals. During the COVID-19 pandemic, the government of Panama went into a nationwide lockdown to limit the spread of the virus. This lockdown resulted in the closing of tourism infrastructure and limited mobility in both land and coastal areas. We used this “natural experiment” as an opportunity to study the impact of tour-boat activities on dolphin communication by using passive acoustic monitoring data collected before and during the lockdown at Dolphin Bay, Bocas del Toro, Panama. During the lockdown, tour-boat activity was absent, but boats transporting people and supplies were allowed to circulate. The shift in type of boat activity within the lockdown resulted in lower ambient noise levels and more frequent detections of dolphin sounds. We also detected a more diverse whistle repertoire during the lockdown than in the pre-lockdown period, even when accounting for variation in sample coverage. A Random Forest Analysis classified whistles between the two periods with high accuracy (92.4% accuracy, κ = 0.85) based primarily on whistle modulation and duration. During the lockdown, whistles were longer in duration and less modulated than pre-lockdown. Our study shows that a shift in boat traffic activity can generate significant changes in dolphin habitat, and in their communicative signals, an important consideration given ongoing unregulated ecotourism in the region.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".