Bibliographic record
Abstract
The recording of raw or streamed data, as done by CGG during MEGATEM and HELITEM surveys, allows for the extraction of passive EM responses, inadvertently recorded during AEM surveys. These include powerline responses in data sets acquired in the vicinity of strong powerlines, VLF responses in data sets recorded with sufficiently high sampling frequencies and potentially AFMAG responses in the frequency range 25-600 Hz.The recording of the three-component AEM data allows for the vector processing of these passive EM responses, including the derivation and modelling of the tipper data. Conductivity information can be derived from the tipper data with an apparent conductivity transformation and, more rigorously, with 2D and 3D inversions that take into account the terrain’s topography.The extraction of passive EM responses is demonstrated on a number of data sets. A powerline apparent-conductivity grid derived from a MEGATEM survey near Timmins, Canada indicates conductivity structures not evident in the corresponding active-source EM data. VLF responses derived from South American MEGATEM and North American HELITEM data show a strong correlation to topography. The former were successfully modelled with 2D and 3D inversions, and the derived shallow conductivity structures confirm and complement the information extracted from the active-source EM 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".