Bibliographic record
Abstract
Abstract : The overall goal of this project has been to provide advanced undergraduates, graduate students, and postdoctoral investigators with a broad understanding of ocean acoustics as well as the techniques used to study the ecology of marine animals in situ. A secondary goal of the project is to provide a setting for developing and testing new technologies. In this manner, it serves as a research magnet, attracting leading scientists to conduct their own research in a creative teaching and learning environment that catalyzes interactions across the various disciplines associated with Bioacoustical Oceanography. The specific objective of this research is to provide students with a broad understanding of the acoustic techniques used to study the distribution and behavior of marine animals in the context of their physical/chemical/biological environment. Highlighted activities conducted over the 5 years of our grant include the following: (1) three zooplankton acoustics cruises to Saanich Inlet, BC, Canada, to demonstrate how the forward problem can be used to groundtruth acoustic data, and how survey data can be interpolated to generate 3-D assessments of zooplankton distributions; (2) experimental study in Saanich Inlet to test the hypothesis that strobe lights on a MOCNESS sampling system reduces or eliminates net avoidance by euphausiids; (3) field trials along the Kohala Coast of Hawaii Island to test a new, multi-frequency echo sounder developed for use with Wave Gliders; (4) a fisheries acoustics cruise to demonstrate the methods used for conducting marine predator-prey studies using acoustics to define prey fields; (5) a passive acoustics exercise at Lime Kiln Lighthouse on San Juan Island to calibrate its hydrophone array and evaluate its performance in localizing and tracking orcas; and; (6) a passive acoustics exercise along the Kohala Coast in which three Wave Gliders were deployed with hydrophones to localize and track vocalizing humpback whales.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".