Statistical analysis of fin whale vocalizations recorded by a seismic network at the Endeavour Segment of Juan de Fuca Ridge, N. E. Pacific Ocean.
Bibliographic record
Abstract
From 2003–2006, an eight-station seafloor seismic network was deployed along the Endeavour Segment of the Juan de Fuca ridge that recorded an extensive data set of 20-Hz fin whale calls. Algorithms have been developed to detect and track vocalizing whales that swim near the seismic network. During the first year of operation, more than 100000 fin calls that include ∼100 whale tracks were identified. Tracks comprise both single whales distinguished by a stereotyped ∼25 s interpulse interval and inferred multiwhale tracks characterized by more complex interpulse intervals. Whale tracks vary from individuals or groups that cross the network in a few hours to those that meander for up to 24 h. The call rates vary seasonally with the highest rates in winter and exhibit an apparent weak diurnal variation. The center frequencies range from 17–34 Hz, with the primary population centered at 20 Hz and a secondary population centered at 25 Hz. Statistical analysis of observed bandwidths and center frequencies, interpulse intervals, seasonality, and diurnal patterns will be presented. Additionally, the ∼100 whale tracks will be used for migration analysis and to quantify the swimming patterns in the network. [Funding from the ONR.]
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 | 0.003 |
| 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 | 0.000 |
| 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".