Bibliographic record
Abstract
Aeromagnetic surveys play an important role in the detection and analysis of terrestrial impact structures as large semi-regional aeromagnetic surveys are widely available.Impact craters can be divided into two groups based on morphostructure, namely simple and complex. Simple craters are relatively small bowl-shaped depressions with an upraised and fractured rim whereas complex craters are larger with a central uplift zone.Magnetic signatures of terrestrial impact craters vary greatly, reflecting the target rocks, the impact-related magnetisation and effects of crater fill and post-impact sediments. In basement rocks, the common signature is a magnetic low, ranging in amplitude from a few nT up to a few hundred nT. The central peak or ring uplift of crushed basement may produce strong magnetic highs. The magnetic signature may be due to shock demagnetisation, shock remagnetisation, and thermal and chemical remanent magnetisation effects. Impact craters in sedimentary targets are usually subdued and amplitudes of a few nT up to 10 nT are common.Enhancement of magnetic signatures of impact structures using filtering techniques is an important part of detection and analysis. Derivatives and shaded relief techniques, along with separation filtering, are probably the most used methods. Algorithms for fractional order derivatives and circular shaded relief have dramatically improved filter results. The fractional derivative order can be varied to optimise separation of the impact magnetic signature. Circular shaded relief treats all directions equally unlike the fade-out for features sub-parallel to the shading direction evident in conventional shading.The fractional order derivative and circular shaded relief algorithms are illustrated from impact structures in Australia and Canada in both basement and sedimentary cover rocks.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".