Formation of Tarim Large Igneous Province and Strengthened Lithosphere Revealed Through Machine Learning
Bibliographic record
Abstract
Abstract Earth history is punctuated by voluminous magmatism and the formation of large igneous provinces (LIPs). Although anomalous mantle temperatures are known to be involved in many LIPs formation, the potentially critical role of fluids remains elusive. Here we apply machine learning methods (e.g., random forest, deep neural network, and support vector machines) to train models based on global datasets of basalts associated with different settings. The trained models predict that the basalts of Tarim LIP in northwestern China show a spatial decrease in their island arc affinity from northeast to southwest, which can be correlated to fluids released from earlier southward oceanic subduction. Temporally, the fluid activity declined from 290 Ma basalts to 270 Ma mafic dykes, suggesting that the fusible components in the mantle source were waning over time and ultimately a strengthened lithosphere was generated. Our study provides new insights into the crucial role of fluids in the generation of LIPs, particularly those related to ancient subducted slabs.
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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 | 0.001 |
| 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) | 0.001 | 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".