{"id":"W2892123877","doi":"10.1145/3264907","title":"ID'em","year":2018,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affordance; Leverage (statistics); Cover (algebra); Scalability; Computer science; Strengths and weaknesses; Context (archaeology); Human–computer interaction; Artificial intelligence; Engineering; Geography; Database; Mechanical engineering","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":[0.001315977,0.0008584592,0.0006548607,0.001083836,0.0009658014,0.003382496,0.00267432,0.002064855,0.07329891],"category_scores_gemma":[0.003414224,0.0005759145,0.0006938375,0.0007989327,0.0008596743,0.004189658,0.004916047,0.001736291,0.04475004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006219441,"about_ca_system_score_gemma":0.0009417515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000775094,"about_ca_topic_score_gemma":0.0011949,"domain_scores_codex":[0.998812,0.000143017,0.00006764521,0.0002743111,0.0005735598,0.0001295807],"domain_scores_gemma":[0.9982806,0.000300212,0.00006474659,0.0006896434,0.0004950273,0.0001697171],"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":[0.0003353222,0.0001872232,0.001607212,0.0007723966,0.00005539132,0.0005059166,0.0005224261,0.002038104,0.03579422,0.07602992,0.260764,0.621388],"study_design_scores_gemma":[0.00003274195,0.00009097753,0.0004743342,0.00007615193,0.00002631929,0.0009185978,0.0001043275,0.01274753,0.03352659,0.007483295,0.9444628,0.00005626793],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006882246,0.001770046,0.7552848,0.00176351,0.002886759,0.0003082042,0.001641645,0.03678332,0.1926796],"genre_scores_gemma":[0.1059618,0.002672422,0.5730855,0.004842136,0.0008057753,0.0006403474,0.006476529,0.005251497,0.300264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07329891,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009772940699900345,"score_gpt":0.2536313500389529,"score_spread":0.2438584093390526,"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."}}