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Record W7062071895

Sea Level Rise and Commercial Office Markets in Southeast Florida

2022· dissertation· en· W7062071895 on OpenAlexaboutno aff

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2022
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsFlood mythFlooding (psychology)Quarter (Canadian coin)RentingSea level riseHurricane katrina
DOInot available

Abstract

fetched live from OpenAlex

Sea level rise is an indisputably mounting predicament that has exacerbated consequences in Southeast Florida. In this thesis, we explore the impacts of sea level rise risk in commercial office markets in Miami-Dade County. We examine 560 commercial office properties with sale price records from 2000 to 2020, and 497 commercial office rental properties from 1988 quarter one through 2020 quarter four. For both sales and rental properties, we analyze each sample comprehensively, then we isolate the respective samples first by historic flood amount and then by flood risk metrics. We conclude by segregating properties in high-risk areas by historic flood amount to eradicate property location as a confounding variable. Our results suggest that properties that have historic exposure to flooding from either or both major recent hurricanes, Katrina in 2005 and Irma in 2017, have lower sales prices and rental values when compared to properties that have not experienced historic hurricane flooding in comparable flood risk zones. Our results also indicate that generally, commercial office properties that are more concentrated near waterfront areas have experienced greater historic flooding and have larger predicted flood risk than properties farther inland.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0240.000

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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