{"id":"W2554979309","doi":"10.1109/mc.2016.375","title":"Interdevice Media: Choreographing Content to Maximize Viewer Engagement","year":2016,"lang":"en","type":"article","venue":"Computer","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"","keywords":"Computer science; Content (measure theory); Multimedia; Content delivery; Media content; Computer graphics (images); World Wide Web; 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":[0.001798597,0.0004876937,0.0001929694,0.001313533,0.0009994078,0.002333183,0.0006464007,0.0006375621,0.00633491],"category_scores_gemma":[0.005976428,0.0001881959,0.0001469599,0.0006569442,0.0008905224,0.002987791,0.00191721,0.0004992249,0.001437754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004320327,"about_ca_system_score_gemma":0.0007020828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006771658,"about_ca_topic_score_gemma":0.002666423,"domain_scores_codex":[0.9992336,0.0004779485,0.00003973425,0.00008354706,0.00009677867,0.00006837954],"domain_scores_gemma":[0.9971927,0.001546706,0.0001899372,0.0003358349,0.0003278292,0.0004069975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006557307,0.0007636443,0.04354971,0.002455215,0.00003706796,0.001052017,0.1832176,0.001193899,0.16132,0.02303246,0.0158356,0.5668871],"study_design_scores_gemma":[0.0002650958,0.002513089,0.1392197,0.001424433,0.0001850639,0.002701454,0.2306401,0.009621491,0.1217674,0.02122155,0.4701236,0.0003170202],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6938894,0.0007805637,0.1679693,0.002218527,0.0002419632,0.001844052,0.0005459994,0.00330883,0.1292014],"genre_scores_gemma":[0.85158,0.0005220302,0.1327991,0.0002641635,0.00007409797,0.0008816904,0.0003168463,0.0005000616,0.0130619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00633491,"threshold_uncertainty_score":0.02119237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1381696163108447,"score_gpt":0.3417400279064831,"score_spread":0.2035704115956384,"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."}}