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Record W3026511322 · doi:10.18280/ijsse.100216

Entropy Weight-Based Matter-Element Extension Model for Security Evaluation and Prewarning Mechanism of National Defense Science and Technology

2020· article· en· W3026511322 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Decision-Making Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsExtension (predicate logic)Entropy (arrow of time)Computer scienceComputer securityMechanism (biology)National securityReliability engineeringSystems engineeringRisk analysis (engineering)EngineeringPolitical scienceBusinessThermodynamicsPhysicsProgramming language

Abstract

fetched live from OpenAlex

National defense science and technology (NDST) provides the driving force and guarantee for the building of a strong army and a prosperous country.Considering the threats to NDST security, this paper explores deep into the security evaluation and prewarning mechanism of NDST.Firstly, an evaluation index system was constructed for NDST security, in the light of the factors affecting NDST security.Next, a matter-element extension model (MEEM) was established based on entropy weight (EW), drawing on industrial security theory and operations research.The established EW-MEEM was applied to evaluate the NDST security of China in 2010-2017.Based on the evaluation results, a prewarning mechanism was built to promote the future development of China's NDST.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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