Advanced Floating S-Plane Inversion (AFSI) Method used to Recover Conductivity and Chargeability from VTEM Data
Bibliographic record
Abstract
Summary This study was carried out in order to test the Advanced Floating S-plane Inversion (ASFI) method on helicopter time domain EM data affected by Airborne Induced Polarization (AIP) in order to extract electrical conductivity and chargeability information. A single profile flown by VTEM system in Northern Ontario was selected for the study. This profile covers well-known geology, as well as it has been previously inverted using original RDI method (2013) and LCI (2015) method based on Cole-Cole parameter extraction, therefore there is significant data available for comparison and validation of newly proposed ASFI algorithm. The results of using ASFI inversion showed good correlation with previously recovered conductivity sections and improvement in recovery of chargeability data, which together with high computational efficiency of ASFI makes it a powerful tool for interpretation of airborne time domain 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".