Novel ways to process and model GEOTEM data
Bibliographic record
Abstract
Data processing methods originally developed for the TEMPEST system allow GEOTEM half-sine data to be deconvolved and transformed to GEOTEM square-wave data. The advantages of the transformed square-wave data are that they refer to a standardised waveform that does not vary through a survey. The high-frequency information contained in the data recorded during the transmitter pulse can be readily utilised and the data can be easily corrected for variations in the transmitter loop height, pitch, roll and receiver coil offset. Modelling results from transformed GEOTEM data acquired across the Bull Creek mineralisation indicate that the transformation works well for survey data.Traditional off-time, single component conductivity - depth modelling of GEOTEM data can be improved by utilizing the full waveform and by inverting multicomponent datasets. In highly conductive terrain, such as above seawater, where system parameters such as the bird position are hard to derive reliably from the time - domain in-phase component as a proxy for the primary field, the joint inversion of multicomponent data helps to correctly resolve layered-earth parameters. Jointly inverting the 3-component on- and off-time data of a GEOTEM bathymetry survey in the Torres Strait showed that the data fit can be greatly improved by allowing the inversion to determine the receiver offset and attitude. This results in greater confidence in the derived conductivity - depth values.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".