Equilibrium Viscosity and Disequilibrium Rheology of a high Magnesium Basalt from Piton De La Fournaise volcano, La Reunion, Indian Ocean, France
Bibliographic record
Abstract
Lava flows are a common hazard at basaltic to intermediate volcanoes in general and have posed a significant threat to La Reunion Island specifically over the past centuries. In sustained flow units, the efficiency of lava transport away from the vent is dominated by cooling. For basaltic to intermediate lavas it is the ability of the lava to solidify during cooling which exerts a first-order control on the extent of flow. As a consequence, understanding the sub-liquidus rheology of lavas has become a key focus in lava flow research in the past decade. To date, a systematic understanding of lava rheology during emplacement conditions has been largely hampered by a lack of experimental data. Here we present new data on the rheological evolution of a crystalizing high Mg basalt from Piton de la Fournaise. Sub-liquidus experiments were performed at constant cooling rates of 0.5 to 5 K/min, to mimic conditions similar to those experienced by lava during flow on the surface and magma during dike and sill emplacement. The data show that the effective viscosity of the crystallizing suspension increases until reaching a specific sub-liquidus temperature, the so-called “rheological cut-off temperature” (Tcutoff), at which the lava effectively solidifies. This departure to high viscosity is a consequence of rapid crystallization and is found to be primarily controlled by the imposed cooling-rate. Based on our experimental data, we adapt the failure forecasting method (FFM), which is commonly used to describe the self-accelerating nature of seismic signals to forecast material failure, to predict the rheological cut-off temperature (Tcutoff). The data presented serve to expand the modest existing experimental database on non-equilibrium rheology of lavas as a step towards understanding the underlying process dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".