Application of the airborne electromagnetic method for Banded Iron-Formation mapping in the Hamersley Province, Western Australia
Bibliographic record
Abstract
Stratigraphy of the Hamersley Province in Western Australia, featuring alternating units of banded iron-formations and shales with contrasting electrical properties in a mostly gently undulating shallow dipping layered geometry, is particularly favourable to airborne time-domain electromagnetic mapping techniques. Manipulated vertical cross-sections of modelled conductivity obtained from laterally constrained 1D inversion of SkyTEM304 data enable the exploration geologist to interpret weathering profiles, shallow dipping stratigraphy and steep structures, all of which are crucial aspects of bedded iron ore deposits genesis models. Five potential interpretation pitfalls have nevertheless been encountered. Occasional obvious artefacts can be present in inverted models but their causative sources should be easily identifiable in the measured channel data. The inverted models coherence is generally compromised when acquisition system terrain clearance cannot be maintained below approximately 80 m. The in-loop setup and processing routines have inherent limitations over steep stratigraphy. Complex stratigraphy geometries can result in off line responses to be artificially incorporated in the 1D inverted conductivity cross-sections. Lastly, lateral interfaces between fresh and altered rocks could easily be misinterpreted as fault contacts.
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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 | 0.000 |
| 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".