{"id":"W6944048340","doi":"10.18130/e1pa-9129","title":"L’intelligence artificielle dans la ville : Renforcer l’engagement civique et la confiance du public","year":2022,"lang":"fr","type":"book","venue":"Libra","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Ministry of Foreign Affairs; Context (archaeology); Limiting; Public policy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006629804,0.0005807462,0.0005171088,0.0002447938,0.0003564624,0.0003531818,0.001009088,0.0006035018,0.01186126],"category_scores_gemma":[0.0001491709,0.0006538609,0.0002549167,0.000330363,0.0005160507,0.0003593404,0.0009627283,0.001900278,0.0002809355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002826488,"about_ca_system_score_gemma":0.0002962226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001129895,"about_ca_topic_score_gemma":0.00006695914,"domain_scores_codex":[0.9975159,0.0002766478,0.0005749144,0.0005809608,0.0003698363,0.0006817746],"domain_scores_gemma":[0.9977775,0.0009751152,0.0001584818,0.0009332852,0.0000363887,0.0001192791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000508019,0.00005256572,0.0002009498,0.000311932,0.0001675672,0.0002089746,0.001451889,0.01856063,0.00007999517,0.933953,0.0339104,0.01109703],"study_design_scores_gemma":[0.00009613411,0.00008151281,0.0001078113,0.0001800728,0.0000354343,0.00006622911,0.003778219,0.009948743,0.001276485,0.009153906,0.9746212,0.000654289],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001755179,0.001584172,0.02345097,0.002736261,0.001496892,0.0004601723,0.0002091369,0.001465249,0.9668419],"genre_scores_gemma":[0.4235525,0.0125669,0.00376407,0.0006534713,0.0005246695,0.0006980395,0.0004773021,0.0004400861,0.557323],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9407108,"threshold_uncertainty_score":0.9995913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349459084108158,"score_gpt":0.2223250354065487,"score_spread":0.1988304445654672,"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."}}