Bibliographic record
Abstract
Abstract We appreciate the opportunity to reply to the concerns expressed in the discussion by M. Fedi and T. Quarta of our article. First, we do acknowledge that in Fedi and Quarta's 1998 paper they showed a 2D wavelet-denoised image for aeromagnetic data. Although we are not in full agreement with several aspects of their articles, nevertheless, we apologize to the authors for the oversight of not including their papers as references in our work. However, we point out that the basis for our method had been published in 1998 (Leblanc et al., 1998) as an expanded abstract that had been submitted to the SEG before Fedi and Quarta's (1998) work had been released. A Ph.D. thesis by Leblanc (1999), which covered various aspects of wavelet denoising of aeromagnetic data and higher-order derivatives, was in the public domain well in advance of publication of the article by Fedi et al. (2000). It is clear that when comparison of the work by Fedi and Quarta (1997, 1998, 2000) to that of our work, each group applied different methodologies to the challenge of denoising geophysical 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 | 0.018 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.059 | 0.072 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".