{"id":"W806991377","doi":"10.4018/ijcini.2014070103","title":"Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks","year":2014,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Big data; Computer science; Data science; Computational intelligence; Collective intelligence; Analytics; Benchmarking; Cloud computing; Sentiment analysis; Fuzzy logic; Set (abstract data type); Data mining; Artificial intelligence","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":[0.002817813,0.0004273161,0.0006053607,0.002644053,0.001009781,0.002279618,0.001054592,0.0008677334,0.0009311479],"category_scores_gemma":[0.02455647,0.0002422587,0.0006145964,0.002725877,0.001702534,0.005805699,0.001481337,0.001187145,0.0002047881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521002,"about_ca_system_score_gemma":0.0005556441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002498549,"about_ca_topic_score_gemma":0.001864379,"domain_scores_codex":[0.997941,0.0008846752,0.00009528349,0.0004570843,0.0004881044,0.0001338891],"domain_scores_gemma":[0.9892511,0.007372685,0.001500751,0.0006693992,0.0009114341,0.0002946488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000175119,0.0001314224,0.03154289,0.0004798313,0.0002175849,0.0006465289,0.001932087,0.1976924,0.00403646,0.6766255,0.00557632,0.08094389],"study_design_scores_gemma":[0.000007018234,0.00003934078,0.0061707,0.00005682208,0.00002182677,0.0001343454,0.0005271251,0.552639,0.0009803404,0.436724,0.00267022,0.00002924102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3035063,0.001611372,0.6806155,0.003322063,0.00008640558,0.000155127,0.001647773,0.0003283671,0.008726999],"genre_scores_gemma":[0.9568058,0.0008046679,0.04046991,0.000184887,0.0001217668,0.000121863,0.0006974079,0.00002418076,0.0007696182],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002817813,"threshold_uncertainty_score":0.01490217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07114705649550866,"score_gpt":0.3194985598557693,"score_spread":0.2483515033602606,"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."}}