A new database of independently estimated eruption source parameters devoted to eruptive column model evaluation
Bibliographic record
Abstract
Eruptive column models are crucial for managing volcanic crises, forecasting future events, and reconstructing past eruptions. Given their central role in volcanology and the large uncertainties weakening their predictions, the evaluation and improvement of these models is critical. Such evaluation is challenging as it requires independent estimates of the main model inputs (e.g. mass eruption rate) and outputs (e.g. column height). Despite recent efforts to extend datasets of independently estimated eruption source parameters (ESP) (e.g. Mastin 2014, Aubry et al. 2017), there is no standardized, maintained, and community-based ESP database devoted to the evaluation of eruptive column models. Here we present a new ESP database designed to respond to the needs of the plume modelling community, and which will also be valuable to observatories, field volcanologists, and volcanic ash advisory centers. We compiled data for over 130 eruptive events with independent estimates of: i) the mass eruption rate; ii) the height reached by the column; and iii) atmospheric conditions during the eruption. In contrast with previous ESP datasets, we distinguish estimates of column height that relate to different phases (ash and SO2) and parts of the column (plume top or umbrella). We additionally provide the total grain size distribution, uncertainties in eruption parameters, and multiple sources for atmospheric profiles for events where these parameters are available. The database also includes a wealth of additional information which will enable modelers to distinguish between different eruptions when evaluating or calibrating models. This includes the type of eruption, the morphology of the plume (weak/transitional/strong), and the occurrence and mass entrained within pyroclastic density currents. We will apply the new database to revisit empirical scaling relationships between the mass eruption rate and “plume height”. In particular, we will show how such relationships depend on the type of height (e.g. SO2 height vs. ash top height) and eruption (e.g. magmatic vs. phreatomagmatic) considered. We will also discuss the difficulties and limitations of compiling ESP estimates from the literature as well as characterizing fundamentally unsteady volcanic events by a single value for each ESP.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".