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Record W2738916184 · doi:10.23977/jeis.2016.11001

Detection Criterion on Airborne Sensor Measure Information Effectiveness

2016· article· en· W2738916184 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

VenueJournal of Electronics and Information Science · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Detection Methods
Canadian institutionsnot available
FundersChinese Aeronautical Establishment
KeywordsResidualCovarianceMeasure (data warehouse)StatisticsBayes' theoremMathematicsObservational errorRange (aeronautics)Computer scienceFuzzy logicBasis (linear algebra)Upper and lower boundsData miningAlgorithmBayesian probabilityArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Based on the detection criterion of traditional single threshold residual information, in order to get likelihood probability, this paper makes residual information fuzzy which is in the range of the upper and lower bounds (UBL for short) of sensor residual information effectiveness that is under incomplete measurement respectively set by steady-state error covariance under incomplete measurement and posterior Cramer-Rao bound (CRB). It advances the detection criteria on the basis of UBL of residual under incomplete measurement on the premise of residual information confidence at the moment according to Bayes formula and accomplishes the simulation.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.744
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.010
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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