Bibliographic record
Abstract
Geyser eruptions are produced by a complex and poorly understood set of subsurface processes and conditions. They typically have an abundant supply of water, relatively permeable and competent subsurface material, a conduit to the surface, a driving mechanism (commonly believed to be the initiation of gas lift pumping by steam formation in the conduit), and a trigger. Here we present time series of dissolved CO 2 concentrations in near-surface discharge waters of a thermal geyser in Yellowstone National Park (northwestern United States) that vary systematically over several eruption cycles. Chemical geothermometry, combined with a temperature profile in a nearby well, suggests that the geyser water ascends from non-boiling conditions (∼153–171 °C at a depth of 57–65 m). When the time series of near-surface measured CO 2 concentrations are extrapolated to these subsurface conditions assuming dominantly adiabatic cooling, the additional gas pressure from dissolved CO 2 is large enough to cause the total dissolved gas pressure to exceed bubbling pressure, inducing bubble formation. We postulate that CO 2 is a necessary component to triggering eruptions in the geyser studied. Furthermore, unlike steam, CO 2 bubbles do not completely re-condense during cooling in the geyser conduit, hence providing better sustenance for gas lift pumping than pure H 2 O boiling.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".