{"id":"W2600883547","doi":"","title":"Visualizing world flows: a challenge between efficacy, accuracy and aesthetics","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Transports","funders":"","keywords":"Visualization; Data visualization; Computer science; Real world data; Data science; Order (exchange); Work (physics); Art world; World map; Artificial intelligence; Geography; History; Engineering; Cartography; Art history; Economics; Performance art","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":[0.02953673,0.001356057,0.001730279,0.005278755,0.001943353,0.02316803,0.003412854,0.003184758,0.01097276],"category_scores_gemma":[0.1196282,0.001191886,0.001206679,0.003033291,0.008195302,0.02263204,0.008379423,0.003758221,0.002675073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001473262,"about_ca_system_score_gemma":0.001052274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001858054,"about_ca_topic_score_gemma":0.001531658,"domain_scores_codex":[0.9745257,0.01673578,0.001425585,0.00197153,0.00488393,0.0004575668],"domain_scores_gemma":[0.9173329,0.06335889,0.002017368,0.009783124,0.006255186,0.001252478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004460113,0.0001479787,0.00546023,0.003881851,0.0003492413,0.0004443725,0.02103222,0.01428756,0.005903509,0.4301548,0.03639276,0.4814994],"study_design_scores_gemma":[0.0001205266,0.0001943587,0.004916141,0.001588463,0.0001747897,0.001260257,0.01300105,0.037068,0.003518215,0.803862,0.1340792,0.0002170303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05410045,0.02433023,0.7577668,0.08930899,0.00232622,0.0003839871,0.001740543,0.003678918,0.06636377],"genre_scores_gemma":[0.5298374,0.01306533,0.4398731,0.002375724,0.002068042,0.0004526425,0.0008143149,0.002832166,0.008681239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02953673,"threshold_uncertainty_score":0.1562071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03554510882839804,"score_gpt":0.2937255092519451,"score_spread":0.258180400423547,"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."}}