Erratum: Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)
Bibliographic record
Abstract
Erratum of the paper ‘Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)’, by Caffagni et al., published in Geophys. J. Int. (2016) 206, 644–658. Hydraulic-fracturing (HF) is a process, widely used in unconventional shale and tight-sand oil–gas reservoirs, which consists of injecting fracturing fluids into a rock formation at a pressure exceeding the fracture pressure of the rock, thus inducing a network of fractures through which oil or natural gas can flow into a wellbore (CCA 2014). This method has the desirable characteristic that it preserves event polarization information. The error arose in the definition of the AGC procedure. Our method remains consistent, and there are no mistakes in it. In eq. (3), the Hilbert amplitude envelope was simply missing. The AGC function is defined as the convolution product between the Hilbert amplitude envelope of the original trace and the triangular smoothing operator Δ(t, tΔ). In addition, the formula with |$\skew3\bar A $| has to be corrected, since it is the average of the amplitudes of the three components of the original trace. In this way, our method really preserves the polarization information.
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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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.027 |
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".