Hierarchical autoregressive error models in trans-dimensional matched-field geoacoustic inversion.
Bibliographic record
Abstract
This paper applies a general trans-dimensional matched-field inversion algorithm to data with strongly correlated errors, which are addressed by augmenting the forward model with a hierarchical autoregressive error model. This approach accounts for the limited knowledge of the optimal seabed parametrization and of the data-error statistics in the resulting geoacoustic parameter uncertainties. The assumed seabed parametrization influences estimates of parameter values and uncertainties since different parametrizations lead to different ranges of data predictions. The data support for a particular model is often non-unique and trans-dimensional formulations account for this by including an unknown model indexing parameter to consider groups of models (indexed by this parameter) in the posterior results. The hierarchical autoregressive error model accounts for a wide range of possible error correlations with few parameters, with no requirement to explicitly specify a covariance matrix (for which an inverse and determinant must be computed) at every Markov chain step, thereby increasing efficiency. This approach is particularly useful for trans-dimensional inversion since point estimates may not be representative of the state space which spans multiple subspaces of different dimensionalities. The order of the autoregressive process required by the data is determined here by posterior residual-sample examination. [Work supported by ONR.]
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".