Acoustic and satellite remote sensing of blue whale seasonality and habitat in the Northeast Pacific
Bibliographic record
Abstract
Northeast Pacific blue whales seasonally migrate, ranging from the waters off Central America to the Gulf of Alaska. Using acoustic and satellite remote sensing, we have continuously monitored the acoustic activity and habitat of blue whales during 1994–2000. Calling blue whales primarily aggregate off the coast of southern and central California in the late summer, coinciding with the timing of the peak euphausiid biomass, their preferred prey. The northward bloom of primary production along the coast and subsequent northbound movements of the blue whales are apparent in the satellite and acoustic records, respectively, with the calling blue whales moving north along the Oregon and Washington coasts to a secondary foraging area with high primary productivity off Vancouver Island in the late fall. El Ni ~no conditions, indicated by elevated sea-surface temperature and depressed regional chlorophyll-a concentrations, are apparent in the satellite records, particularly in the Southern California Bight during 1997/1998. These conditions disrupt biological production and alter the presence of calling blue whales in primary feeding locations. Remote sensing using acoustics is well suited to characterizing the seasonal movements and relative abundance of the northeast Pacific blue whales, and remote sensing using satellites allows for monitoring their habitat. These technologies are invaluable because of their ability to provide continuous large-scale spatial and temporal coverage of the blue whale migration. r 2004 Elsevier Ltd. All rights reserved. 1.
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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 | 0.002 |
| 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 | 0.000 |
| 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".