Microclimates in Fumarole Ice Caves on Volcanic Edifices—Mount Rainier, Washington, USA
Bibliographic record
Abstract
Abstract The fumarole ice caves of Mount Rainier in the Cascade Volcanic Arc in Washington, USA, provide unique insight into the dynamic equilibrium between thermal flux of fumaroles on volcanic edifices and snow accumulation on summit glaciers. More than 3.5 km of surveyed cave passage nearly circumnavigate the East Crater, reaching within 19 m of the 4,392 m summit and extending to 144 m depth along the glacier‐crater boundary. The large circum‐crater passage connects entrances on the crater rim to steep transverse passages, and cave morphology is maintained by fumarole gas convection and advection. A melt‐ and condensate‐formed lake, Lake Adélie, occupies a portion of the circum‐crater passage. Hourly data were collected between August 2016 and August 2017 and included the measured temperatures at three fumaroles, the cave air temperature and pressure, the lake water temperature and depth, and the outside temperature and snow depth at Paradise Visitors Center. Time‐series analyses of these data reveal complex associations between synoptic to seasonal weather, fumarole activity, and lake level. On seasonal and longer scales, fumarole temperatures follow independent pathways connected to spatial and temporal changes in volcanic heat flux and the circulation of glacial melt. Major snowfall seals the cave entrances, increasing cave air temperature and pressure from fumarole output and causing rising lake levels from increased melt until entrances reopen. Repeating freeze‐thaw cycles observed in the cave monitoring data are a primary cause of crater mass wasting. Despite these transient variations, the scale and morphology of the caves is preserved over decadal or longer scales.
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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 | 0.001 | 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".