{"id":"W1586577127","doi":"10.4000/vertigo.13736","title":"Pour le meilleur et pour le pire ! Les arbres en ville peuvent-ils faire patrimoine ? Analyse des spatialités concurrentes arbres-riverains à Grenoble","year":2013,"lang":"fr","type":"article","venue":"VertigO","topic":"French Urban and Social Studies","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008289803,0.0003700622,0.0004047449,0.001257994,0.00224827,0.006453543,0.0008060076,0.0007280625,0.01108734],"category_scores_gemma":[0.002326573,0.0002787705,0.0004687421,0.002686932,0.002860397,0.002254454,0.001498347,0.0008244252,0.001521057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005496993,"about_ca_system_score_gemma":0.003170789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2357117,"about_ca_topic_score_gemma":0.3881059,"domain_scores_codex":[0.9990256,0.000323339,0.00002577653,0.0002308535,0.0001969476,0.0001975064],"domain_scores_gemma":[0.9986362,0.0005181764,0.0001775356,0.0001300591,0.0004040863,0.0001339997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000359341,0.0001410075,0.2722399,0.0008617492,0.0002368099,0.005033631,0.3643144,0.006681907,0.01474087,0.1556231,0.01066545,0.1691018],"study_design_scores_gemma":[0.00001287204,0.00008080524,0.4207847,0.0004696768,0.00008407722,0.0005279606,0.3486601,0.003344615,0.001904056,0.01521449,0.2088264,0.00009026175],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8998801,0.001780396,0.01147746,0.003085444,0.00006350355,0.00006912566,0.0005694968,0.0001775104,0.08289696],"genre_scores_gemma":[0.9621831,0.0006398048,0.004922974,0.0001714364,0.0000151122,0.00004594721,0.000276093,0.0001247206,0.03162068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2357117,"threshold_uncertainty_score":0.4686794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04322202833583044,"score_gpt":0.2815346114709151,"score_spread":0.2383125831350847,"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."}}