An Assessment Of Coastal Impacts To The Natural And Built Assets Of The New York State Office Of Parks, Recreation And Historic Preservation
Bibliographic record
Abstract
Climate change is expected to cause an increase in heavy precipitation events, flooding, and sea level rise in New York State, which will have significant impacts on coastal areas. Almost half of New York's state parks and historic sites, which are managed by the New York State Office of Parks, Recreation and Historic Preservation (OPRHP), are located in major coastal areas, including the Atlantic Ocean, Hudson River, Lake Erie, and Lake Ontario. As the effects of climate change become more pronounced in these areas, a selection of OPRHP’s infrastructure and natural, cultural, and historic resources will be at risk. To identify which of OPRHP’s facilities are at greatest risk of coastal storms and flooding, a multi-part GIS assessment was conducted, and an interactive tool was developed to make the results accessible to the agency. The assessment was based on three existing GIS-based Coastal Risk Area Models produced by the New York State Department of State (DOS), which utilized a variety of data including storm surge, sea level rise, and erosion, to identify coastal areas of extreme, high, and moderate risk. The first part of the assessment involved identifying the overlap of coastal risk areas in OPRHP facilities and subsequently ranking the facilities according to the anticipated coastal impacts. The second part of this assessment analyzed the existing natural and built assets within park facilities to determine locations of greatest risk for coastal biodiversity and priority infrastructure, respectively. This included the development of five new GIS models: Density of Key Built Assets, Density of Key Natural Assets, Built Assets at Risk, Natural Assets at Risk, and Natural & Built Assets at Risk. Finally, the assessment results were made available through online platforms so that the information can be used to support planning and prioritization of resilience efforts by OPRHP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".