The estimation of geoacoustic parameters via low frequencies (50–100 Hz) for selected Shallow Water 06 test data.
Bibliographic record
Abstract
This work will demonstrate the geoacoustic inversion “success” on data of using only one or two low frequencies, multiple ranges, and multiple realizations for geoacoustic inversion of actual SW06 data. The data used are the same as those processed by Jiang and Chapman and involves three ranges (1, 3, and 5 km) and multi-tonal continuous wave data collected on a 16 phone vertical array. Multiple realizations of the data were used where each reduced the non-uniqueness a bit. The multiple ranges and frequencies also reduced the non-uniqueness of the suggested solutions. However, there still remains a significant number (hundreds) of possible “solutions,” (values of ctop, cbot, hsed, and chsp) for which MFP<0.9 (including those suggested by Jiang and Chapman). The use of higher frequencies requires refinement of more parameters (such as the ocean sound-speed profile, source depth and range, water depth, and phone locations) but would not necessarily improve estimates of such bottom parameters as chsp and hsed. Thus, non-uniqueness of bottom parameters is an issue which may well exist for all inversion approaches.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".