Vocalizations of bearded seals (<scp><i>Erignathus barbatus</i></scp>) and their influence on the soundscape of the western Canadian Arctic
Bibliographic record
Abstract
Abstract The soundscape is a crucial habitat feature for marine mammals. This study investigates the contribution of bearded seal vocalizations to the soundscape in the western Canadian Arctic, and also the vocal characteristics of bearded seals relative to sea ice conditions. Passive acoustic data were recorded near Sachs Harbour between August 2015 and July 2016. Sound pressure levels (SPL) in the 50–1,000 Hz and 1–10 kHz bands increased as the total duration of all bearded seal vocalizations increased, and this relationship was moderated by sea ice concentration. Bearded seals in this region had an overlapping vocal repertoire with bearded seals in other areas of the Arctic, and had seven additional vocalizations that have not been previously documented for this region. This study is the first detailed assessment of the influence of bearded seal calls on SPL, which shows the high potential of bearded seals to influence underwater sound levels during the mating season. Bearded seals live in a changing Arctic seascape, and their influence on the soundscape may shift as sea ice continues to diminish. It is imperative that acoustic monitoring continues within the Arctic, and this study provides a baseline for future monitoring as the Arctic continues to change.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".