Determining the sensitivity of measured acoustic quantities on variability in geophysical and oceanographic conditions
Bibliographic record
Abstract
Sonar is used to remotely investigate the underwater environment and to detect and track vessels therein, either by their own acoustic emissions or through scattering from active transmission. The acoustic signals available to an observer are a function of the transmitter’s relative position, course, and signature, and the local environment. In order to make effective use of the received acoustic signals, an observer requires a thorough understanding of the propagation and scattering characteristics of the overall underwater environment as well as the implications of those characteristics on the analysis techniques applied to the received signals. Defense R&D Canadas (DRDC) Rapid Environmental Assessment (REA) Program aims to provide a capability for accumulation and interpretation of environmental information in a tactical timeframe. A main objective of the REA program is to explore the nature of geophysical and oceanographic variability and to quantify its effect on acoutic signals. This effect is analyzed via modeling and through the use of data from the joint DRDC/NURC sea trial BASE 04 (Broadband Acoustic Sonar Experiment 2004). This trial, which took place in May and June of 2004 in the Malta Plateau and Medina Bank areas, included several experiments measuring active sonar propagation in an uncertain environment.
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 | 0.002 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".