Estimating the parameters of simple models from two-component on-time airborne electromagnetic data
Bibliographic record
Abstract
ABSTRACT The horizontal and vertical components of an on-time electromagnetic (EM) response can be used to estimate the parameters of simple models such as thin sheets, half-spaces, thin sheets over a lower half-space, and a two-layer model. The formulas used in these methods are valid in areas where the on-time response is essentially proportional to the conductivity or conductance, the so-called “resistive limit.” The half-space and thin sheet over lower half-space models can be combined to give an estimate of the conductivity for a lower half-space below a thick sheet that might be reasonable for the entire survey area. With this estimation, an equation solver can be used to estimate the thickness and conductivity of the overlying thick sheet over the whole survey area. This latter approach seemed most appropriate for the Russell South area in the Athabasca Basin, Canada, where GEOTEM data have been collected. The output of the algorithm was generally stable. Although it did not always reliably reproduce the overburden thicknesses as measured in a set of reference drillholes, it did give an estimate that was reasonable in the relatively conductive areas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".