Environmental inversion and matched-field tracking with a surface ship and an L-shaped receiver array
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acoustic data from the natural broadband signature of a quiet surface ship, recorded on the vertical leg of an L-shaped array, is used to invert for the local geo-acoustic parameters and the resulting effective environment is used for subsequent tracking of the surface ship using a matched-field tracking technique applied to the full array. The matched-field analysis includes a comparison of the incoherent product of the processed data from the horizontal and vertical subapertures with coherent processing of the data from the full L-shaped array. Subaperture processing is of interest since there is a (loose) requirement that the number of data snapshots be greater than or equal to the number of array elements. This presents averaging difficulties for large arrays when the source being observed is moving. Analyzing each array leg separately allows the use of a smaller number of snapshots from which averaged quantities are constructed. Taken separately, the vertical leg of the array provides range-depth information, while the horizontal leg provides bearing information. The incoherent product of each leg is compared to processing the full array coherently illustrating that the incoherent product generally worked as well, or better than, processing the full array, producing compact maxima at the ship location, and producing fewer false source locations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it