Post-typhoon assessment of surface greenness disturbance using Landsat series observations
Bibliographic record
Abstract
Extreme climate events are projected to increase under the context of global warming associated with the increase in greenhouse gas emissions. In particular, coastal regions which are also characterized with significant urbanization will be vulnerable to the extreme events, such as severe typhoon. Timely assessment and accurate information on the extent and severity of the damage caused by extreme event is necessary to better facilitate decision-making for disaster alleviation and post-recovery. Satellite remote sensing has an important advantage in assessing the impacts caused by extreme events on natural environment and socio-economic dimension at a variety of spatial and temporal scales. Surface greenness disturbance mainly over Xiamen, China, caused by Typhoon Meranti (2016) was investigated using a pair of observations by Landsat 7 ETM+ and Landsat 8 OLI. Significant decreases in greenness were detected, which mainly located along the Maluan Bay and the settlements in suburban and rural areas surrounding the track of typhoon.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".