{"id":"W3017264441","doi":"10.1089/big.2020.29034.cfp","title":"<i>Call for Special Issue Papers:</i> Multimedia Big Data Analytics for Engineering Education","year":2020,"lang":"en","type":"article","venue":"Big Data","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brandon University","funders":"","keywords":"Big data; Mainstream; Analytics; Computer science; Learning analytics; Multimedia; Data science; Engineering education; World Wide Web; Engineering; Engineering management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002790242,0.0002202858,0.0002322903,0.0001029958,0.0001178942,0.0003934363,0.00243183,0.00009109534,0.0001306928],"category_scores_gemma":[0.001863743,0.0002133606,0.00003967512,0.000451654,0.00003227783,0.001440837,0.001548679,0.00009791612,0.0002251687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001299369,"about_ca_system_score_gemma":0.0001046348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001224451,"about_ca_topic_score_gemma":0.0001549757,"domain_scores_codex":[0.9983907,0.000002503634,0.0003280172,0.0007508905,0.0002081887,0.0003197188],"domain_scores_gemma":[0.9979528,0.0001077598,0.0001555065,0.001575633,0.0001710061,0.00003735765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004298734,0.00005936091,0.0001340103,0.0005014566,0.00002378832,6.323616e-7,0.0000116393,0.00001309686,0.0003466903,0.0002629018,0.5781133,0.4204901],"study_design_scores_gemma":[0.0002691316,0.000006330443,0.0001573552,0.00004841396,0.0001347956,4.966279e-7,0.00004566987,0.1324595,0.00007395921,0.0000373409,0.8665162,0.0002507739],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"editorial","genre_scores_codex":[0.001253895,0.001575836,0.8298303,0.05804822,0.05319443,0.006698421,0.03332705,0.001070713,0.01500107],"genre_scores_gemma":[0.05844072,0.0003815128,0.0445868,0.02345839,0.6174803,0.0001957535,0.2534072,0.0002914097,0.001757858],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7852436,"threshold_uncertainty_score":0.8700594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2486039153975927,"score_gpt":0.3175989241080752,"score_spread":0.06899500871048247,"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."}}