Bibliographic record
Abstract
Abstract : This project was a joint collaboration between Defence Research and Development Canada--Atlantic (DRDC Atlantic) and the Applied Research Laboratory of Pennsylvania State University (ARL/PSU) to analyze and model reverberation, target echo, and clutter data in shallow water. The primary outputs of the collaboration were manuscripts for publication in conference proceedings and refereed journals. Secondary outputs were improved models and algorithms. This report provides a summary for the full 5-year period of the grant: 2006-2011. Overall, the main accomplishments of the 5-year period were as follows: (1) submission and publication of journal articles on the Rapid Environmental Assessment reverberation methodology that had been the focus of measurement and modeling efforts over the previous decade; (2) participation in the ONR Reverberation Modeling Workshops and the development of benchmark solutions for some of the problems; (3) extension of the fast reverberation modeling approach to handle range-dependent environments and bistatic geometry; (4) development of a Clutter Model for direct comparison with towed array beam time series in a range-dependent clutter environment; (5) initial work on modeling target echo and time spreading of the target echo; (6) extension of the normal-mode reverberation modeling approach to handle sub-bottom reverberation, including range-dependent environments; and (7) design of experiments, participation in sea trials, model-data comparisons, and analysis of towed array reverberation and clutter data. The year-by-year accomplishments are listed in the Main Accomplishments section of the report.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".