Vocal sequences in narwhals (<i>Monodon monoceros</i>)
Bibliographic record
Abstract
Sequences are indicative of signal complexity in vocal communication. While vocal sequences are well-described in birds and terrestrial mammals, the extent to which marine mammals use them is less well understood. This study documents the first known examples of sequence use in the narwhal (Monodon monoceros), a gregarious Arctic cetacean. Eight female narwhals were fitted with animal-borne recording devices, resulting in one of the largest datasets of narwhal acoustic behaviour to date. A combination of visual and quantitative classification procedures was used to test whether subjectively defined vocalization patterns were organized into sequences. Next, acoustic characteristics were analyzed to assess whether sequences could disclose group or individual identity. Finally, generalized linear models were used to investigate the behavioural context under which sequences were produced. Two types of sequences, consisting of "paired" patterns and "burst pulse series," were identified. Sequences of burst pulse series were typically produced in periods of high vocal activity, whereas the opposite was true for sequences of paired patterns, suggesting different functions for each. These findings extend the set of odontocetes which are known to use vocal sequences. Inquiry into vocal sequences in other understudied marine mammals may provide further insights into the evolution of vocal communication.
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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 | 0.000 |
| 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 | 0.000 |
| 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".