Assessment of the Relationship Between Repose Period and Eruption Magnitude of Gamalama Volcano for Community Preparedness in Ternate Island – Indonesia
Bibliographic record
Abstract
Volcanic eruptions from Gamalama volcano often threaten Ternate Island, a small volcanic island with more than 200,000 inhabitants in North Maluku Province.Ternate Island has an area of 111.4 km 2 , and the distance between settlements and the crater ranges from 4-7 km.With those conditions, Ternate Island has become one of the areas in Indonesia with a high risk of volcanic hazards.As one of the efforts to reduce volcanic risk, it is essential to understand the characteristics of volcanoes, including the relationship between the repose period and the eruption magnitude.Historical data on the eruption of the Gamalama volcano from 1510-2018 were collected from various reliable sources and analyzed statistically to determine their relationship.The results show no significant relationship between the repose period and the magnitude of the eruption.The results also show that the eruption's average repose time from 1510-2018 is unreliable and cannot be generalized.However, one thing that is quite interesting to be studied further is related to the positive relationship between the length of the repose period and the impact of the resulting eruption.This incident was recorded in several eruptions at Gamalama volcano, such as in 1960Gamalama volcano, such as in , 1980Gamalama volcano, such as in , 2003Gamalama volcano, such as in , and 2011.These findings require people living on Ternate Island to increase their preparedness and practice in dealing with eruption disasters following the contingency plan from the disaster management authority.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".