A Tale of Change and Continuity: Three Storm Surges, and Three Towns, Under Three Flags
Bibliographic record
Abstract
The historical record, though incomplete, shows that typhoon generated storm surges cause extreme damage and loss of life in the Philippine archipelago. Storm surges associated with typhoons historically make sea-to-land crossings. There is an annual average of nineteen tropical cyclones occurring in the Philippine’s area of responsibility, of which an average of nine cross the country. There are few areas of the archipelago that have not been affected by storm surges. This paper investigates the crucial role and impacts of this natural hazard in certain areas of the Philippines that have been exposed to typhoons and storm surges across the centuries. The paper discusses the character of the storm surge, highlights some of the worst storm surge catastrophes that have occurred outside the Philippines, and then focuses on three storm surge events in the Visayan Islands of Samar and Leyte. On October 12, 1897, November 24-26, 1912, and November 3-11, 2013, the exposed coastal towns of Hernani and Guiuan on Samar and Tacloban on Leyte were destroyed by storm surges. The recurrent damage and loss of life caused by storm surges and cyclonic storms has increased in these three places as the complex cascade chain of the hazard changed through time, shifting from thousands to millions of people displaced and their livelihoods and communities destroyed on three occasions between October 1897 and November 2013.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".