A mid-crustal tipping point between silica-undersaturated and silica-oversaturated magmas
Bibliographic record
Abstract
Abstract Alkaline–silicate igneous complexes contain a huge diversity of rock types, ranging from silica-undersaturated (feldspathoid-normative) to silica-oversaturated (quartz-normative) compositions. At present, the controls on the formation of such compositional diversity are poorly quantified. Here we apply thermodynamic models to investigate these controls using a case study of the Blatchford Lake Igneous Complex (Canada), which is compositionally representative of worldwide alkaline–silicate systems. By modelling fractionation of a primitive mafic melt across crustal pressures, we identify a narrow (~0.5 kbar) ‘tipping point’ across which residual melts become silica-rich or alkali-rich when shallower or deeper, respectively. This tipping point is consistently present at mid-crustal pressures (3–5 kbar; ~10–15 km depth) for a range of viable primitive melts, moving to higher pressures within this range for more hydrous and more oxidized melts. Crystallization at these pressures (within barometric estimates for the complex) can therefore generate and explain the vast diversity of observed alkali-rich and silica-rich compositions. A similar tipping point is also present in other modelled mafic igneous systems at mid-crustal conditions, indicating it is a widespread phenomenon. This result implies a key role for mid-crustal mafic staging chambers in generating compositional diversity in alkaline–silicate complexes worldwide.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".