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Record W2100639794 · doi:10.1287/inte.1050.0127

A Florida County Locates Disaster Recovery Centers

2005· article· en· W2100639794 on OpenAlexaff
Jamie Dekle, Mariel S. Lavieri, Erica L. Martin, H. Emir-Farinas, Richard L. Francis

Bibliographic record

VenueINFORMS Journal on Applied Analytics · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFacility Location and Emergency Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsResidenceAgency (philosophy)Emergency managementMileTransport engineeringOperations managementEngineeringGeographyOperations researchBusinessPolitical science

Abstract

fetched live from OpenAlex

In 2001, the Federal Emergency Management Agency (FEMA) required every Florida county to identify potential locations of disaster recovery centers (DRCs). The DRCs are to be opened and staffed by FEMA personnel, subsequent to any declared disaster. The Emergency Management Division of the Alachua County Department of Fire/Rescue Services sponsored a project to identify potential DRC sites. The project team used a mathematical analysis tool called the covering location model in a two-stage approach to find, recommend, and have accepted DRC locations. The “stage 1” approach gave three idealized DRC locations requiring each residence in the county to be within 20 miles of the closest DRC. Next, the team relaxed the 20-mile requirement and identified locations close to the “stage 1” locations that also satisfied evaluation criteria not included in stage 1. The “stage 2” results provided significant improvements to the original FEMA location criteria, while maintaining acceptable travel distances to the nearest DRC.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.019
GPT teacher head0.221
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations91
Published2005
Admission routes1
Has abstractyes

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