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Record W4400928679 · doi:10.59429/ima.v2i1.6375

E-FireGuard: Empowering firefighters through innovative E-commerce solutions

2024· article· en· W4400928679 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.

Bibliographic record

VenueIndustrial Management Advances · 2024
Typearticle
Languageen
FieldComputer Science
TopicMobile and Web Applications
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsBusinessProcess managementComputer scienceKnowledge management

Abstract

fetched live from OpenAlex

For firefighters to remain safe and successful in ever-more-complex and tough firefighting situations, they must have access to state-of-the-art equipment. Nevertheless, obtaining the newest firefighting technology is frequently hampered by conventional procurement techniques. This article introduces “E-FireGuard,” a cutting edge E-commerce platform created to solve these issues and provide firemen with simple access to top- notch gear. Firefighters may easily browse, buy, and evaluate a variety of firefighting goods online with E-FireGuard. Procurement procedures may be expedited because to the platform's user-friendly design, extensive product selection, and safe payment alternatives. Moreover, E-FireGuard facilitates the adoption of cutting-edge technology and best practices by acting as a center for cooperation, information exchange, and innovation among firefighters.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.313
Teacher spread0.260 · 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