{"id":"W2460776840","doi":"10.1145/2936744.2956677","title":"KineMaster","year":2016,"lang":"en","type":"article","venue":"","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Computer science; Encoding (memory); Decoding methods; Multimedia; Mobile device; Mobile computing; Human–computer interaction; Embedded system; World Wide Web; Operating system; Telecommunications; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007217561,0.000928933,0.0009195103,0.001214674,0.001079652,0.002188636,0.001619765,0.0009150786,0.3570572],"category_scores_gemma":[0.002559914,0.0006355634,0.0005003103,0.0009894803,0.0005425143,0.002265784,0.002972133,0.001019973,0.1485349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007139052,"about_ca_system_score_gemma":0.001631259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003914706,"about_ca_topic_score_gemma":0.007243718,"domain_scores_codex":[0.999146,0.0000654595,0.00004463984,0.0002376746,0.0004205779,0.00008564464],"domain_scores_gemma":[0.9986505,0.0001777665,0.00005798228,0.0002461385,0.0005940273,0.0002735544],"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":[0.00171796,0.0001994024,0.002540454,0.001065865,0.00005179107,0.0007560134,0.0005793334,0.0008760751,0.04738921,0.01398893,0.5951636,0.3356714],"study_design_scores_gemma":[0.0002052436,0.0002846771,0.002397163,0.00009375379,0.00005819451,0.0007008618,0.000188742,0.003404879,0.009721022,0.001772684,0.9811119,0.00006080879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02252642,0.002761669,0.1345378,0.002299822,0.001972821,0.001378212,0.02325208,0.128603,0.6826682],"genre_scores_gemma":[0.1197565,0.002004477,0.09044021,0.00169472,0.0004660915,0.0009242129,0.03759082,0.01737347,0.7297494],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6429428,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03875873177858664,"score_gpt":0.3528357542974886,"score_spread":0.314077022518902,"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."}}