Characterizing Transmission Loss Variability During the Target and Reverberation Experiment 2013
Bibliographic record
Abstract
A significant driver of uncertainty in sonar performance is the variability in underwater acoustical propagation caused by environmental fluctuations and uncertainty in the position of sources, targets, and receivers. A set of echo-repeat experiments was conducted during the Target and Reverberation Experiment 2013 (TREX13), a sea trial that took place in April to May 2013 in the Gulf of Mexico near Panama City, FL, USA. The variability in measured transmission loss (TL) was characterized using two different methods: Variability with respect to a mean observed TL, and variability with respect to modeled TL. Both one-way and quasi-reciprocal two-way TL measurements at 2250 and 7500 Hz were analyzed to characterize the variability at timescales ranging from less than one second to several days, with the results indicating that the acoustic propagation fluctuates stochastically on all these time scales. The results of statistical tests suggest that the TL variability can be treated as Gaussian fluctuations about a central TL obtained from an acoustic propagation model, with standard deviations of 5 dB over timescales up to one day, or 10 dB over timescales from one to six days.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".