The Role of Continental Alkaline Magmatism in Mantle Carbon Outflux Constrained by a Machine Learning Analysis of Zircon
Bibliographic record
Abstract
Abstract Continental alkaline magmatism has been suggested to play a significant role in releasing deep mantle carbon into the atmosphere, which can greatly impact the global climate. However, the temporal variations of alkaline magmatism and their potential to modulate climate over geologic time remain poorly constrained. The detrital zircon record is a frequently used proxy for tracking secular variations in particular magmatism. Here, we use a novel machine‐learning technique to discriminate zircon from carbonatites, kimberlites, and other alkaline rocks. A global compilation of detrital zircon yields continental alkaline magmatic flare‐ups between 1,050−850, 650−500, 250−200, and 50−0 Ma. Our estimates indicate relatively elevated contributions of total magmatic carbon outgassing from alkaline magmatism during the aforementioned magmatic flare‐ups. We infer that anomalous alkaline magmatism may influence global warming during specific intervals of geologic time, but when they are not that voluminous or persistent extensive arc magmatism may drive warming conditions.
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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 | 0.001 | 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 | 0.001 | 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".