Correlating measured oceanographic and acoustic variability toward understanding uncertainty transfer
Bibliographic record
Abstract
The Recognized Environmental Picture experiment 2011 (REP11) was conducted to support research in the areas of battlespace characterization, quantifying uncertainty, and decision support. By integrating results from these three areas, the goal is to incorporate physics based uncertainty transfer in models driving decision support tools. Generically, this is accomplished by parameterizing the environment according to need and providing the best knowledge available for each parameter including uncertainty. Experimentally, the research requires information on both the input and output sides of acoustic propagation models used for tactical prediction or decision aids. This talk describes an experimental effort to contemporaneously measure both oceanographic and acoustic quantities over a wide range of spatio-temporal scales using a combination of mobile, autonomous, and fixed assets. By measuring these quantities over the course of several days and repeating set geometries, many realizations of the oceanographic and acoustic fields were obtained. These data will be examined with respect to correlations between observed variability in the environment and variability in the measured acoustic fields.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".