Experimental validation of regularized array element localization
Bibliographic record
Abstract
This paper examines and validates regularized inversion for array element localization (AEL) by quantitative comparison of inversion results to direct measurements of receiver positions for a full-scale AEL survey. Regularized AEL treats both receiver and source positions as unknown parameters in a ray-based inversion; prior information on source/receiver positions, inter-receiver spacing in depth, and/or a smooth array shape can be included, subject to statistically fitting the acoustic data. Uncertainties in the recovered receiver positions are estimated via Monte Carlo appraisal. To study this approach, a specially stabilized, two-dimensional receiver array and a series of impulsive sources (imploding glass light bulbs) were deployed from shore-fast (motionless) Arctic sea ice. Sources and recordings were not synchronized in time, so AEL inversions are based on relative arrival times. Receiver positions were measured to an uncertainty of ∼5 cm in each dimension [9 cm in three dimensions (3D)] using nonacoustic (optical) methods. Average AEL errors (difference between measured receiver positions and inversion results) of 13 cm in depth, 27 cm in the horizontal, and 30 cm in 3D, as well as good agreement between the measured errors and estimated AEL uncertainties validate the regularized approach and provide benchmarks for acoustic AEL. Receiver-position errors are quantitatively investigated as a function of the number of sources, source-position errors, and different regularizations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".