REEXAMINING THE RELATIONSHIP BETWEEN BODY SIZE AND TONAL SIGNALS FREQUENCY IN WHALES: A COMPARATIVE APPROACH USING A NOVEL PHYLOGENY
Bibliographic record
Abstract
Abstract A negative relationship between cetacean body size and tonal sound minimum and maximum frequencies has been demonstrated in several studies using standard statistical approaches where species are considered independent data points. Such studies, however, fail to account for known dependencies among related species—shared similarity due to common ancestry. Here we test these hypotheses by generating the most complete species level cetacean phylogeny to date, which we then use to reconstruct the evolutionary history of body size and standard tonal sounds parameters (minimum, maximum, and center frequency). Our results show that when phylogenetic relationships are considered the correlation between body size (length or mass) and minimum frequency is corroborated with approximately 27% of the variation in tonal sound frequency being explained by body size compared to 86% to 93% explained when phylogenetic relationships are not considered. Central frequency also correlates with body size in toothed whales, but for other tonal sound frequency parameters, including maximum frequency, this hypothesized correlation disappears. Therefore, constraints imposed by body size seem to have played a role in the evolution of minimum frequency but alternative hypotheses are required to explain variation in maximum frequency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".