{"id":"W6894146156","doi":"10.5281/zenodo.829886","title":"Proceedings Of The 8Th Workshop On Semantic Ambient Media Experiences (Same 2016): Smart Cities For Better Living With Hci And Ux - Seachi In Conjunction With The International Conference On Human-Computer Interaction (Chi)","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conjunction (astronomy); Software deployment; Assisted living; Everyday life; Ambient intelligence; Social media; Smart environment; Panel discussion","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001778361,0.00009722577,0.00008695132,0.00009812032,0.0007696672,0.0006049207,0.0005080781,0.00003179129,0.0001779568],"category_scores_gemma":[0.00009335142,0.00005911851,0.00001573627,0.00006492336,0.0002769312,0.0002490877,0.000265508,0.0001892965,0.00001074066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004929662,"about_ca_system_score_gemma":0.000001478728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001523586,"about_ca_topic_score_gemma":0.0000133918,"domain_scores_codex":[0.9993842,0.00001482863,0.0001070305,0.0001670227,0.0001930177,0.0001338982],"domain_scores_gemma":[0.9995394,0.00003793096,0.00008462898,0.0001674319,0.0001519293,0.00001864842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001894318,0.0008163454,0.01277894,0.00123608,0.001033047,0.00001527753,0.2298643,0.004794223,0.02542946,0.1587169,0.2595174,0.3039037],"study_design_scores_gemma":[0.00536307,0.004547013,0.3686906,0.007646885,0.0001214733,0.0002145937,0.2283719,0.1015377,0.02220345,0.001565386,0.2579496,0.001788429],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989624,0.000005176011,0.0005502987,0.001232481,0.0001501227,0.0002697854,0.000008893093,0.0001526385,0.008006571],"genre_scores_gemma":[0.9995198,0.00002463541,0.00007249918,0.00005454124,0.00007336467,8.875209e-7,0.0000120955,0.0001298209,0.0001124167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3559116,"threshold_uncertainty_score":0.5919735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157130946414832,"score_gpt":0.2411429166320552,"score_spread":0.1995716071679069,"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."}}