Bibliographic record
Abstract
This paper describes a complete approach to the inversion of ocean acoustic data for environmental model parameters, which is also applicable to other inverse problems. Within a Bayesian formulation, the general solution is given by the posterior probability density (PPD) of the model parameters, which includes both data and prior information. Properties of the PPD, such as optimal parameter estimates, variance/covariance, inter-parameter correlations, and marginal probability distributions, are computed numerically for nonlinear inverse problems using Markov-chain Monte Carlo (MCMC) importance sampling methods. Since the data uncertainty distribution (including measurement and theory errors) is generally not known a priori, a simple, physically-reasonable form, such as a Gaussian or double-exponential distribution, is assumed, with statistical properties estimated from data residual analysis. In many cases, the full error covariance matrix (including off-diagonal terms) is required, and in some cases effects of nonstationary errors must be included. If biased data errors are suspected, additional unknown parameters representing the biases are included explicitly in the inversion. The validity/applicability of the above assumptions and estimates is examined a posteriori by applying both qualitative and quantitative statistical tests. New advances in efficient and adaptive MCMC sampling for nonlinear inversion will be presented.
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.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".