Quantifying spatial and temporal variation of North Pacific fin whale (<i>Balaenoptera physalus</i>) acoustic behavior
Bibliographic record
Abstract
Abstract In order to help develop hypotheses of connectivity among North Pacific fin whales, we examine recordings from 10 regions collected in the spring and fall. We develop a Random Forest model to classify fin whale note types that avoids manual note classification errors. We also present a method that objectively quantifies the note and pattern composition of recordings. We find that fin whale recordings near Hawaii have distinctive patterns, similar to those found in other regions in the central North Pacific, suggesting potential migration pathways. Our results are consistent with previous studies that suggest there may be two different populations utilizing the Chukchi Sea and central Aleutians in the fall and mix to some degree in the southern Bering Sea. Conversely, we found little difference between spring and fall recordings in the eastern Gulf of Alaska, suggesting some residency of whales in this region. This is likely due to fine scale similarities of calls among the inshore regions of British Columbia, while offshore areas are being utilized by whales traveling from various distant areas. This study shows how our novel approach to characterize recordings is an objective and informative way to standardize spatial and temporal comparisons of fin whale recordings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".