Vulnerability Assessment to Climate Change of Households from Mabacan, Sta. Cruz and Balanac Watersheds in Laguna, Philippines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Province of Laguna has been identified as one of the most vulnerable to climate change. Despite the various efforts of the local government unit, the province still suffers massive damages brought about by typhoons, flooding and landslides. This signals the need for a better strategy to manage climate change related hazards. As a first step, it is necessary to characterize the vulnerability of households in the province. This study contributed towards this end a descriptive analysis of household exposure to impacts of climate related hazards and estimating a household’s vulnerability index using the Vulnerability as Expected Poverty (VEP) approach. The mean VEP for a per capita monthly poverty threshold of US$1.25 is 37%, 41% for US$1.5 and 46% for US$2.0. Among the different sectors, those dependent on aquaculture/fishery had the highest incidence of vulnerability followed by those dependent on employment in the manufacturing sector. In terms of geographical location, households in the coastal areas were found to have the highest incidence, followed by those in the lowland and lastly those in the midland to highland areas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it