A statistical-data quality-control methodology for large, nonconventional DC resistivity data sets
Bibliographic record
Abstract
Developments in multi-channel instrumentation systems and computational resources have made the collection of large, non-conventional DC resistivity datasets commonplace. Although these large datasets can greatly improve the resolution of recovered inverse models, they present challenges for standard data quality control (QC) methodologies. These limitations prompted us to develop a generalized data QC methodology which utilizes statistical analysis and classification tools to identify inconsistent and noise contaminated data. The use of these statistical methods helps decrease the subjectivity of the data QC process. We test the methodology on a field dataset from an underground potash mine and several synthetic datasets. The results of these tests show that the methodology is capable of identifying and characterizing highly noise contaminated data from a variety of noise sources. This 4-stage data QC methodology is highly adaptive, allowing it to be tailored to accommodate data from any type of DC resistivity survey and potentially data from a variety of other geophysical surveys. Presentation Date: Wednesday, October 19, 2016 Start Time: 2:45:00 PM Location: 174 Presentation Type: ORAL
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".