{"id":"W2807006973","doi":"10.1145/3209281.3209385","title":"Spatial, temporal and semantic contextualization of citizen participation","year":2018,"lang":"en","type":"article","venue":"","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Contextualization; Computer science; Natural language processing; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002667549,0.00003689375,0.00006005894,0.00003251281,0.00001539813,0.000006863731,0.0000220402,0.00003150712,0.00007401267],"category_scores_gemma":[0.00001900659,0.00003263137,0.000006628886,0.00004732379,0.00006484312,0.00003998082,0.00001180487,0.0000135391,0.000005075434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003779134,"about_ca_system_score_gemma":0.000001602568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001422619,"about_ca_topic_score_gemma":0.0004923433,"domain_scores_codex":[0.9997727,0.000002878943,0.00008991218,0.0000401023,0.0000350975,0.00005936346],"domain_scores_gemma":[0.9998782,0.0000111829,0.00001175172,0.00006277418,0.00002671633,0.000009400331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004291753,0.000070626,0.6352758,0.0007001265,0.0001838415,0.000003665828,0.004063029,0.0006557817,0.03686862,0.1081774,0.01872632,0.1952319],"study_design_scores_gemma":[0.0009635452,0.0004737927,0.1958703,0.00007698635,0.00004831624,0.000004437329,0.001626921,0.4347518,0.3519512,0.007883186,0.005932799,0.0004167497],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9306253,0.00005424287,0.06467637,0.00003697046,0.00008384239,0.00005152383,0.000001444362,0.0002562695,0.004214089],"genre_scores_gemma":[0.9995981,0.00001801456,0.0002862772,0.00001077784,0.00002660815,0.000003596683,0.000003086758,0.000005488587,0.00004798566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4394055,"threshold_uncertainty_score":0.1330669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673003933126209,"score_gpt":0.2351888692857443,"score_spread":0.2184588299544822,"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."}}