MétaCan
Menu
Back to cohort
Record W2144648064 · doi:10.5555/2675983.2676337

2 Canadian forces flying training school (2 CFFTS) resource allocation simulation tool

2013· article· en· W2144648064 on OpenAlexaffabout
René Séguin, C.H. Hunter

Bibliographic record

VenueWinter Simulation Conference · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsTraining (meteorology)Resource allocationDuration (music)Resource (disambiguation)Computer scienceSimulationResource management (computing)Operations researchAeronauticsEngineeringMeteorologyDistributed computing

Abstract

fetched live from OpenAlex

2 Canadian Forces Flying Training School is responsible for the intermediate phases of all pilot training for the Royal Canadian Air Force. The school's operation is stochastic and dynamic in nature and a resource allocation planning tool has been built to simulate the interactions of its various components. For example, it takes into account weather, aircraft, simulator and instructor availability, and student failure. This paper gives an overview of the school's operation, describes how it is simulated with a custom built C++ application and shows how the tool has been used to estimate average course duration, to determine what resources are the most significant bottlenecks and to study the impacts of significant proposed changes to the way pilots are trained. The tool was instrumental in showing that one resource was clearly responsible for creating bottlenecks and was used to analyze a few mitigation options.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0440.004

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 head0.186
GPT teacher head0.383
Teacher spread0.196 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueWinter Simulation ConferenceSame topicScheduling and Timetabling SolutionsFrench-language works237,207