{"id":"W1967777696","doi":"10.3138/carto.49.2.1393","title":"Mapping Mental Representations of Industrial Risk: Illustrated with the Populations of the Estuary of the Seine River, France","year":2014,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Social Representations and Identity","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subconscious; Mental mapping; Worry; Psychology; Perception; Subject (documents); Mental representation; Geography; Data science; Social psychology; Computer science; Cognition; Medicine; Library science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000854289,0.0001069617,0.0001389979,0.0002693556,0.0007597692,0.00007205775,0.0005116755,0.0000833529,0.00003017731],"category_scores_gemma":[0.0003359633,0.00005335139,0.0002350468,0.001081054,0.000651783,0.0003674683,0.00007550306,0.0002353075,3.30969e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001069854,"about_ca_system_score_gemma":0.00005599837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001025665,"about_ca_topic_score_gemma":0.0001932617,"domain_scores_codex":[0.9982637,0.0002878369,0.0006658998,0.00008871585,0.000577241,0.0001166327],"domain_scores_gemma":[0.9969426,0.0002395686,0.001453212,0.0002652538,0.001071302,0.00002810555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001712574,0.000108091,0.887261,0.00001577111,0.0005720999,5.437503e-8,0.01491216,0.002294001,0.0001507617,0.08415385,0.003193345,0.007167582],"study_design_scores_gemma":[0.001959986,0.0001040721,0.970685,0.0000949949,0.0001637099,0.00002758411,0.009936735,0.002951739,0.0001955701,0.006093672,0.007683257,0.0001036967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830074,0.00007796563,0.01129052,0.002622955,0.001712986,0.0006964261,0.00013939,0.000008809391,0.0004435813],"genre_scores_gemma":[0.9993939,0.00009028315,0.00005051407,0.0001731846,0.0001239525,0.00003344572,0.00005782714,0.000006293889,0.00007056106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08342396,"threshold_uncertainty_score":0.5843607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372818236953426,"score_gpt":0.320868836827483,"score_spread":0.2971406544579487,"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."}}