{"id":"W2291547918","doi":"","title":"Les stratégies d'anticipation des \"effets\" territoriaux des grands équipements de transport: le cas du TGV Rhin-Rhône","year":2013,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"French Urban and Social Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Northern Studies","funders":"","keywords":"Anticipation (artificial intelligence); Humanities; Political science; Philosophy; Computer science","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":[0.003126301,0.001152552,0.0004087433,0.0008870753,0.003218726,0.006064658,0.001091343,0.003211898,0.01836635],"category_scores_gemma":[0.009662976,0.000336129,0.0005522633,0.0007152825,0.002544846,0.002408055,0.00324376,0.002443115,0.002050203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003243147,"about_ca_system_score_gemma":0.004623339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03321449,"about_ca_topic_score_gemma":0.03502802,"domain_scores_codex":[0.9973239,0.001605641,0.00004989589,0.0001960358,0.0003598891,0.0004645655],"domain_scores_gemma":[0.9948471,0.002576929,0.0007096177,0.000213679,0.0008020772,0.0008505802],"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":[0.001897707,0.0007929398,0.06529494,0.00181228,0.0004534159,0.01075403,0.1865424,0.01501249,0.01056863,0.2377098,0.06816739,0.4009941],"study_design_scores_gemma":[0.0002170131,0.001575191,0.138158,0.001997167,0.0004138359,0.003067777,0.3853919,0.01847882,0.006791275,0.08014333,0.3634248,0.0003409085],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5996233,0.005990664,0.02968683,0.03812124,0.0007348197,0.0002759324,0.0004272865,0.00061824,0.3245218],"genre_scores_gemma":[0.973577,0.001228381,0.003551987,0.0003452601,0.00005193756,0.00008242962,0.00006702696,0.0000652851,0.02103068],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03321449,"threshold_uncertainty_score":0.0660423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03698455291280334,"score_gpt":0.2649101428094217,"score_spread":0.2279255898966183,"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."}}