Gravity and magnetic modelling of layered mafic–ultramafic intrusions in large igneous province plume centre regions: case studies from the 1.27 Ga Mackenzie, 1.38 Ga Kunene–Kibaran, 0.06 Ga Deccan, and 0.13–0.08 Ga High Arctic events
Bibliographic record
Abstract
Gravity and magnetic data from the global EGM2008 and EMAG2 datasets are used to identify geophysical anomalies in large igneous province (LIP) plume centre regions with the goal of characterizing mafic–ultramafic intrusions linked to those LIPs. Geophysical anomalies within 18 LIPs distributed globally are investigated. Four of these LIPs are selected for detailed modelling: the 1.27 Ga Mackenzie, 1.38 Ga Kunene–Kibaran, 0.06 Ga Deccan, and 0.13–0.08 Ga High Arctic LIPs. We recognize three spatial distribution types for intrusions in plume centre regions. These are (1) intrusions emplaced along a circular fault system that circumscribes the plume centre, (2) intrusions emplaced along linear rifts that, in some cases, converge towards the plume centre, and (3) single or unclassified intrusions. Modelling supports that the geophysical anomalies associated with these LIPs tend to be produced by large (radius >30 km) and deep-seated crustal intrusions, with densities consistent with mafic–ultramafic rock and magnetic susceptibilities consistent with serpentinized ultramafic rock, except within the Deccan where intrusions are smaller, mainly mafic in composition, and positioned at shallower depths in the crust.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".