Evidence and identification of SPM in airborne TDEM data from Greenland
Bibliographic record
Abstract
Data acquired by airborne in-loop time-domain electromagnetic (EM) systems, such as VTEMTM (Witherly et al., 2004), reflect mainly three physical phenomena in the earth: (1) EM induction, related to ground conductivity, (2) Airborne Inductively Induced Polarization (AIIP), related to the relaxation of polarized charges in the ground (Kratzer & Macnae 2012, Kwan et al., 2015) and (3) Superparamagnetism (SPM), related to the induced secondary magnetic field of certain ultrafine to fine grained magnetic materials (Kratzer, Macnae & Mutton 2013, Sattel & Mutton 2015). It is also called magnetic relaxation or magnetic viscosity. Olhoeft and Strangway (1974) predicted that the SPM effect could become relatively more important and permit mapping of magnetite content with an active, more sophisticated EM system. However, as a background effect it could also represent an additional source of signal to further complicate EM exploration. In this abstract, the general characteristics of SPM anomalies occurring over barren rocks in Greenland, in an environment different from Australia and Southern Africa, are discussed and a method of identifying them in VTEM data will be presented. Presentation Date: Tuesday, October 18, 2016 Start Time: 3:45:00 PM Location: 168 Presentation Type: ORAL
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".