High-Precision Geochronology of LIP Intrusions: Records of Magma–Sediment Interaction
Bibliographic record
Abstract
Reconstructing the tempo and emplacement mechanisms of large igneous provinces (LIPs) and establishing potential links to environmental change and biological crises requires detailed and targeted high-precision geochronology. Contact metamorphism during LIP intrusive magmatism can release large volumes of thermogenic gas, so determining the timing of these events relative to global climate change is crucial. The most reliable age information comes from U-Pb geochronology; however, LIP mafic igneous rocks do not commonly crystallize U-bearing minerals, such as zircon or baddeleyite. Recent work has shown that U-rich minerals can crystallize in fractionated melt pockets in intrusive components of LIPs after contamination of the melt by sedimentary rocks at emplacement level. Zircon and baddeleyite from these pockets make high-precision U-Pb geochronology of LIPs possible, but these unique mechanisms add other complexities.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".