Evolution, Causes and Influence Factors of Taal Volcanic Activities
Bibliographic record
Abstract
Abstract Volcanoes are a kind of geological feature which can bring both destruction and wealth to human beings. This study takes the eruption of Taal Volcano on January 12, 2020 as an example to analyze its eruption evolution, causes and influence factors via QGIS software. Taal Volcano lies at the southwestern end of a convergent boundary between the Eurasian and Philippine Sea tectonic plates where volcanic activities are frequent. Results show that the evolution of the eruption consists of increased CO2 flux, seismic swarms, phreatic explosion chronically. The origin of the volcano is the subduction of the oceanic plate and terrestrial plate. Volcanic eruptions are mostly due to pressurization by active convergent plates activities. The eruption emitted tephra and gas, which exerted impacts on the atmosphere, the nearby vegetation and the water body, and was predicted to result in an El Nino. High concentration of particles, dispersed tephra output, a sharp increase in SO2 and CO content, variation in atmospheric ozone, and rise in humidity were observed in the atmosphere following the eruption. The volcanic output wiped out the plant cover on the volcano island, and covered the vegetation outside of the volcano island, as shown in the RGB band composite and land cover change monitoring images generated using QGIS from Sentinel-2 data. The volcanic output’s influences on nearby water bodies were shown through drops in ocean salinity and Taal lake’s PH, variation in ocean temperature, and increased ocean’s surface latent heat flux.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".