{"id":"W2738916184","doi":"10.23977/jeis.2016.11001","title":"Detection Criterion on Airborne Sensor Measure Information Effectiveness","year":2016,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Aeronautical Establishment","keywords":"Residual; Covariance; Measure (data warehouse); Statistics; Bayes' theorem; Mathematics; Observational error; Range (aeronautics); Computer science; Fuzzy logic; Basis (linear algebra); Upper and lower bounds; Data mining; Algorithm; Bayesian probability; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003831024,0.0008134708,0.001048567,0.002193362,0.0007016155,0.001889007,0.001511734,0.00146336,0.001343865],"category_scores_gemma":[0.0239272,0.0003513219,0.0008181409,0.0007624548,0.002230001,0.004267452,0.002194874,0.001326627,0.0002501965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423796,"about_ca_system_score_gemma":0.000913508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158343,"about_ca_topic_score_gemma":0.0004627553,"domain_scores_codex":[0.994774,0.0008403325,0.0003653736,0.001235318,0.002469863,0.0003150027],"domain_scores_gemma":[0.9882728,0.007051893,0.001104279,0.0008064951,0.002465189,0.0002993792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008790803,0.0001716488,0.01834229,0.00114373,0.0002836031,0.000806666,0.001479453,0.3052746,0.06605177,0.3382304,0.003127141,0.2642097],"study_design_scores_gemma":[0.00004323366,0.0004219989,0.006301641,0.0001319627,0.0001025229,0.001011693,0.0002239425,0.9005207,0.03368156,0.05398568,0.003420227,0.0001549149],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05222691,0.0008572955,0.9415866,0.0003159867,0.0000703345,0.00003832505,0.00008104219,0.0002535721,0.004569932],"genre_scores_gemma":[0.9226421,0.0004541545,0.07508978,0.0001338728,0.0001254112,0.00008391502,0.0001345412,0.00006135145,0.001275016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003831024,"threshold_uncertainty_score":0.02026063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009903932534040571,"score_gpt":0.2518271220800462,"score_spread":0.2419231895460056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}