Modeling lava flow propagation over a flat landscape by using MrLavaLoba: the case of the 2014–2015 eruption at Holuhraun, Iceland
Bibliographic record
Abstract
During the emplacement of the 2014-2015 lava flow in Holuhraun (Iceland) a new code for the simulation of lava flows (MrLavaLoba) was developed and tested. MrLavaLoba is a probabilistic code which derives the area likely to be inundated and the thickness of the final lava deposit. The flow field in Holuhraun progressed through a fairly flat floodplain, and the initial limited availability of topographic data was challenging, forcing us to develop specific modeling strategies. The development of the code, as well as simulation tests, continued after the end of the eruption, and latest results largely benefitted from the availability of improved topographic data. MrLavaLoba simulations of the Holuhraun scenario are compared with detailed observational analyses derived from the literature. The obtained results highlight that small-scale morphological features in the pre-emplacement topography can strongly influence the propagation of the flow. The distribution of the volume settling throughout the extension of the flow field turned out to be very important, and strongly affects the fit between the simulated and the real extent of the flow field. The performed analysis suggests that an improvement in the code should allow adaptable calibration during the course of the eruption in order to mimic different emplacement styles in different phases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".