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Potential Micro Launcher VLM

2012· article· en· W37068719 on OpenAlexfundno aff
Étienne Dumont, Martin Sippel, Emmanuelle David, Trivailo Olga

Bibliographic record

VenueNeuroImage · 2012
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
FundersDepartment of Psychiatry, University of TorontoHealth Resources and Services AdministrationAlberta InnovatesNational Institute of Mental HealthOntario Brain InstituteHospital for Sick ChildrenCanadian Institutes of Health ResearchCentre for Addiction and Mental Health FoundationAzrieli FoundationAutism SpeaksUniversity of TorontoNational Institutes of HealthQueen's UniversityFondation Brain CanadaHolland Bloorview Kids Rehabilitation Hospital FoundationLawson Health Research Institute
KeywordsPayload (computing)PropellantAeronauticsAerospace engineeringParametric statisticsEngineeringSpace launchStage (stratigraphy)Launch vehicleEnvironmental scienceAutomotive engineeringComputer scienceGeologyMathematics

Abstract

fetched live from OpenAlex

This report sums up the orbital performance assessment of the VLM-1 launch vehicle, under study at IAE (the Brazilian Institute of Aeronautics and Space). This launcher, which is proposed to also launch SHEFEX III, is based on three solid propellant motors. The two first stages are based on a S50 stage which still has to be developed and the third stage is the S44 already in operation.
\nIt also includes an overview of existing launchers with a payload capacity of less than 1200 kg in LEO and a parametric development cost estimation of VLM-1.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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 designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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