Bibliographic record
Abstract
DIrectional Frequency Analysis and Recording (DIFAR) sonobuoys have been used by the Navy for many decades, providing magnetic bearings to low frequency (less than 4 kHz) sound sources from a single sensor.Computing advances have made this acoustic sensor technology increasingly easy to use and more powerful.The information presented here is intended to help new users determine when DIFAR sensors are or are not appropriate in whale acoustics research.Acoustic detection ranges for baleen whales average near 20 km but vary from 5 to 100 km depending on conditions.Radio reception range from DIFAR sonobuoys to a typical research vessel averages 18 km with an omni directional antenna on the ship and standard antenna on the sonobuoy.DIFAR bearing accuracy is analyzed for a set o f whale calls where the track o f the whale was well known.Bearings from the DIFAR sensor were found to have a standard deviation of 2.1 degrees.Systematic error and magnetic deviation can be removed using DIFAR bearings to the sound o f the research vessel at a known location.A DIFAR sensor array requires fewer sensors than a conventional hydrophone array and sometimes provides more accurate source locations than the "time o f arrival" hyperbolic methods used with conventional hydrophones.Continuous sounds such as ships are more easily localized with DIFAR sensors than with conventional hydrophones, because it is often difficult to find transient features upon which to estimate the time differences needed for hyperbolic fixing with a conventional hydrophone array.DIFAR hydrophone systems are well suited to right, blue, minke, fin and other baleen whale calls, as well as numerous other sound sources including ships.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".