Acoustic particle velocity and intensity calculations from tri-axial pressure gradient measurements
Bibliographic record
Abstract
In July 2004 Fisheries and Oceans Canada supported a study to investigate effects of seismic airgun signals on hearing organs of freshwater fish in the Mackenzie River at Inuvik, NWT Canada. The study required particle velocity measurements for correlation with observed biological effects. JASCO Research built a pressure gradient measurement apparatus consisting of four hydrophones mounted at the vertices of a triangular-pyramid frame. The system was used to measure differential pressure from the airgun events simultaneously in three perpendicular axial directions. An attached depth-compass sensor monitored the depth and orientation of the system. Hydrophone separations were chosen to be small relative to the acoustic wavelength so that measured differential pressures correctly approximated the pressure gradients along each axis. Particle accelerations were computed directly from pressure gradients following Euler’s linearized momentum equation, and particle velocities were computed by integrating particle accelerations. Acoustic intensity was computed from the product of acoustic pressure and particle velocity. The hydrophone precision imposed a limit on accuracy of particle velocity measurements at low frequencies. Likewise the fixed hydrophone spacings defined an upper frequency limit for applicability of this method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".