Environmental effects following a seismic sequence: the 2019 Cotabato - Davao del Sur (Philippines) earthquakes
Bibliographic record
Abstract
Abstract In the period of October – December 2019, the Cotabato – Davao del Sur region (Philippines) was hit by a seismic sequence comprising four earthquakes with magnitude M W > 6.0 (EQ1-4; max magnitude M W 6.8). The earthquakes triggered widespread environmental effects, including landslides and liquefaction features. We documented such effects by means of field surveys, which we supplemented with landslide mapping from satellite images. Field surveys allowed us to gather information on 43 points after EQ1, 202 points after EQs2-3 and 87 points after EQ4. Additionally, we built a multi-temporal inventory of landslides from remote sensing, comprising 190 slope movements triggered by EQ1, 4737 after EQs2-3, and 5666 at the end of the sequence. We assigned an intensity value to each environmental effect using the Environmental Seismic Intensity (ESI-07) scale. Our preferred estimates of ESI-07 epicentral intensity are VIII for the first earthquake and IX at the end of the sequence, which is in broad agreement with other events of similar magnitude globally. This study, which is the first case of the application of the ESI-07 scale to a seismic sequence in the Philippines, shows that repeated documentation of environmental damage and the evaluation of the progression through time may be useful for time-dependent susceptibility assessment and for investigating the role played by ground shaking and by other mechanisms (e.g., rainfall).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".