Trans-dimensional range-dependent geoacoustic inversion using modal dispersion data in the South China Sea
Bibliographic record
Abstract
This paper presents geoacoustic inversion of modal dispersion data in the South China Sea using a single hydrophone and multiple impulsive sources at ranges from 5–100 km along a shallow-water track with slowly varying bathymetry. As a first step, the source waveform and bubble pulse are deconvolved from the recorded time series using a short-range source recording, corrected for the surface reflection. A time-frequency warping analysis is used to filter individual modes and obtain dispersion (arrival time as a function of frequency) data for three modes. Trans-dimensional Bayesian inversion is applied to the modal dispersion data, based on probabilistic sampling over an unknown number of seabed layers. Range-dependent inversion is considered, based on separating the environment into a sequence of range-independent sections, with frequency-dependent modal propagation times summed over segments. The inversion results are compared to core samples collected at sites along the survey line and to an independent headwave arrival-time analysis of the impulsive-source data.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".