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Record W2043737049 · doi:10.2118/150431-ms

A Stack Model and Capabilities Approach to Investigate Integrated Operations Across Different Industrial Sectors – O&G Industry versus Aviation, Military and Medicine

2012· article· en· W2043737049 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

VenueSPE Intelligent Energy International · 2012
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsAviationStack (abstract data type)Military aviationPetroleum industryEnhanced Data Rates for GSM EvolutionEngineeringComputer scienceOperations researchTelecommunicationsAerospace engineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract This paper adopts a capability and stack model approach to a study of what the oil and gas industry can learn from three selected sectors: military, medicine, and aviation. First, the paper gives an introduction to the stack and capability approach to IO. Next, a preview of common challenges across sectors is provided. Finally, the paper gives examples of cutting-edge developments in medicine, military and aviation, and demonstrates potential learning for the oil and gas industry.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.061
GPT teacher head0.291
Teacher spread0.230 · 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