{"id":"W2561687423","doi":"10.1007/978-3-319-51204-4_24","title":"Entropy-Based Recommendation Trust Model for Machine to Machine Communications","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Communication source; Entropy (arrow of time); Trustworthiness; Node (physics); Data mining; Consistency (knowledge bases); Similarity (geometry); Machine learning; Artificial intelligence; Computer security; Computer network","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00383333,0.0009534523,0.002031208,0.001550052,0.0008133805,0.002117136,0.003135199,0.001939864,0.005183852],"category_scores_gemma":[0.01343811,0.0005896352,0.001379468,0.001822481,0.001468861,0.005891962,0.001694712,0.002610475,0.001220416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002887244,"about_ca_system_score_gemma":0.001664061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009752061,"about_ca_topic_score_gemma":0.01095823,"domain_scores_codex":[0.9967253,0.001251764,0.0002232065,0.0005743447,0.0009160967,0.0003092811],"domain_scores_gemma":[0.9911317,0.005497988,0.0005525558,0.001126148,0.001430934,0.0002605888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000440249,0.0002167996,0.002397808,0.0002132519,0.0002586716,0.0001854087,0.0002537288,0.6964015,0.001795267,0.1897714,0.006764052,0.1013019],"study_design_scores_gemma":[0.000006399322,0.00002339802,0.0001408885,0.000007570261,0.00001663942,0.00002231701,0.000007693238,0.9749045,0.0001789128,0.02436063,0.0003201607,0.00001087789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01779252,0.0004731931,0.9782427,0.0005986898,0.00007009723,0.00007046755,0.0003111794,0.000353193,0.002087987],"genre_scores_gemma":[0.8742248,0.0008185605,0.1106634,0.0002314176,0.0002999435,0.0002218619,0.0008431328,0.000110958,0.01258583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009752061,"threshold_uncertainty_score":0.02094853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04293385832991072,"score_gpt":0.2740269813005879,"score_spread":0.2310931229706772,"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."}}