Stacked sills forming a deep melt-mush feeder conduit beneath Axial Seamount
Bibliographic record
Abstract
Abstract Magmatic systems are composed of melt accumulations and crystal mush that evolve with melt transport, contributing to igneous processes, volcano dynamics, and eruption triggering. Geophysical studies of active volcanoes have revealed details of shallow-level melt reservoirs, but little is known about fine-scale melt distribution at deeper levels dominated by crystal mush. Here, we present new seismic reflection images from Axial Seamount, northeastern Pacific Ocean, revealing a 3–5-km-wide conduit of vertically stacked melt lenses, with near-regular spacing of 300–450 m extending into the inferred mush zone of the mid-to-lower crust. This column of lenses underlies the shallowest melt-rich portion of the upper-crustal magma reservoir, where three dike intrusion and eruption events initiated. The pipe-like zone is similar in geometry and depth extent to the volcano inflation source modeled from geodetic records, and we infer that melt ascent by porous flow focused within the melt lens conduit led to the inflation-triggered eruptions. The multiple near-horizontal lenses are interpreted as melt-rich layers formed via mush compaction, an interpretation supported by one-dimensional numerical models of porous flow in a viscoelastic matrix.
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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 | 0.000 |
| 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".