Evaluating the vulnerability of farming communities to winter storms in Iowa, US
Bibliographic record
Abstract
Winter storms have been reported as the second-most frequent catastrophe in the Midwest of the United States and can create non-negligible impacts on farming communities that highly rely on climatic-sensitive resources and activities. However, few studies have attempted to assess the vulnerability to winter storms in rural contexts. Focusing on all counties in Iowa, US, as the study area, this research aimed to evaluate the vulnerability of farming communities to winter storms and its major determinants. It first identified both climatic and non-climatic indicators for quantifying winter storm exposure, sensitivity, and adaptive capacity by reviewing previous related studies and examining qualitative interview results. Then, spatial analysis tools were used to quantify and aggregate several indicators, such as winter temperature variation, natural shelter, energy capacity, and facility density. Next, factor analysis was employed to identify latent variables and estimate the index score for adaptive capacity. Finally, the vulnerability of Iowa counties to winter storms was calculated and mapped. The results showed that the determinants of adaptive capacity to winter storms in Iowa could be explained as farming economic status, environmental institutional capital, and innovative capital. Overall, high vulnerability was found in Southeast Iowa due to its low farming economic status and innovative capital, and Northwest Iowa as a result of high exposure and low environmental institutional capital. In a state with dominant farming communities, whether to include its major metropolitan areas to assess winter storm vulnerability seemed to only affect the evaluation of the general pattern of adaptive capacity but not exposure or sensitivity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".