Bibliographic record
Abstract
Objectives of this chapter In this chapter, we consider only the cooling of magmas and do not investigate melt generation. Magmas intrude the crust and may accumulate in large reservoirs. Many processes occur in these reservoirs, involving replenishment by melts with compositions that may change with time, crystal settling, compositional convection as well as late-stage equilibration with meta-somatic fluids percolating through already solidified cumulates. We shall focus on the thermal aspects of crystallization. We begin with an analysis of latent heat release due to solidification. We evaluate how long magma reservoirs can remain active and feed eruptions. A few features of crustal magma reservoirs Dimensions and time scales Crustal magma reservoirs can be studied in the field in two different ways: by using erupted lavas on the one hand and studying plutonic bodies brought to the surface by erosion on the other. There is a lingering controversy about the relationship between the two because it is not clear that all plutons were once volcanic reservoirs feeding eruptions. There is no doubt, however, that most volcanic systems involve at least one storage zone, such that large volumes of crystallized magma must be left at depth in order to account for the changes of lava composition that occur. We can therefore deduce from age determinations on lavas over what length of time magmatic systems remain active. For example, Mount Adams, state of Washington, erupted lavas for more than 500,000 years (Hildreth and Lanphere, 1994).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".