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Record W6947872650 · doi:10.4224/20386392

FiRECAM™ deployment survey results

2000· report· en· W6947872650 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2000
Typereport
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware deploymentSample (material)SoftwareService (business)Risk managementFire safety

Abstract

fetched live from OpenAlex

The FiRECAM team, of the Fire Risk Management Program at NRC, conducted a survey to assess the level of interest of the fire safety community in obtaining a copy of the FiRECAM software. In January 2000, a survey was sent to a sample of 700 Canadian engineers, consultants, buildings officials, fire service personnel and fire risk managers. Among the returned surveys, 80 respondents, or 74%, were interested in obtaining a copy of the FiRECAM software through a license. Just over two-thirds of the respondents interested in a copy of FiRECAM, were prepared to pay an additional fee for technical support and training. Most of the respondents who were interested in the software were engineer/consultants or building officials, who indicated that they would use FiRECAM for design and regulation. Close to half of all respondents expressed interest in future collaboration with the FiRECAM team in the development of other advanced fire risk models.

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)0.005
metaresearch head score (Gemma)0.015
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 score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.016

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 head0.082
GPT teacher head0.334
Teacher spread0.252 · 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
Published2000
Admission routes2
Has abstractyes

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Same venueNPARCSame topicWood and Agarwood ResearchFrench-language works237,207