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Record W4410801726 · doi:10.54014/rbvb-nftp

An Assessment Of Coastal Impacts To The Natural And Built Assets Of The New York State Office Of Parks, Recreation And Historic Preservation

2023· dissertation· en· W4410801726 on OpenAlexaboutno aff
Kelsey Ruffino

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationNatural (archaeology)State (computer science)GeographyEnvironmental planningEnvironmental resource managementEnvironmental protectionCivil engineeringEngineeringArchaeologyEnvironmental sciencePolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.442

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.014
GPT teacher head0.273
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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