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Record W2897880226 · doi:10.5055/jem.2018.0382

Analysis of 9-1-1 call data from an emergency management perspective: A case study of the city of Lethbridge

2018· article· en· W2897880226 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Emergency Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsLethbridge College
Fundersnot available
KeywordsLandlineEmergency managementEmergency responseGeographyPerspective (graphical)Medical emergencyEnvironmental planningComputer scienceMedicinePolitical sciencePhone

Abstract

fetched live from OpenAlex

This article examines 9-1-1 call data of the City of Lethbridge in Alberta, Canada over a year to find discernible spatial and temporal trends that may be useful to emergency response or better delivery of emergency management services. The spatial analysis includes Geographic Information Systems hotspot analysis of cellular and landline emergency calls with respect to critical (emergency and healthcare) facilities as well as emergency calls from residential landlines. The temporal analysis looks at hourly, daily, and monthly patterns of emergency calls and the factors driving up the call volumes in certain periods. It also examines emergency call volumes before, during, and after a major disaster (wildfire) event. The article concludes with summarizing the findings and identifying the areas for future research.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.104
GPT teacher head0.429
Teacher spread0.325 · 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