Bibliographic record
Abstract
In the practical application of localizing micro seismic events in potash mines, a total signal energy (TSE) algorithm described in this paper can yield satisfying source location estimates directly, without having to do any parameter estimation first, as most traditional two-step localization algorithms requiring TDOA (time difference of arrival) estimation do. Using Matlab simulations, the TSE algorithm is compared with the approximated maximum likelihood (AML) algorithm, which is also a one-step algorithm and is equivalent to the maximum cross-correlation criterion in single source cases. Then a performance comparison is done between the two one-step algorithms just mentioned and a two-step direct search (DS) algorithm utilizing the Nelder-Mead simplex method to find the source location with the aid of estimated TDOA's. In our simulation of the DS algorithm, the TDOA's are estimated using the generalized cross correlation (GCC) technique. Simulation results showed that the DS algorithm has the worst performance and the TSE algorithm has an overall best performance in the case of locating a near-field, single acoustic source with a short time duration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".