{"id":"W2078642016","doi":"10.3138/carto.44.3.159","title":"fMRI and Human Subjects Research in Cartography","year":2009,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Functional magnetic resonance imaging; Cartography; Perception; Window (computing); Data science; Computer science; Psychology; Geography; Neuroscience; World Wide Web","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":[0.01467314,0.0005041144,0.0008849562,0.003543295,0.001131208,0.002779456,0.001043338,0.002499782,0.00879113],"category_scores_gemma":[0.02867159,0.0004801285,0.0003980068,0.004468191,0.009241147,0.002942506,0.001280639,0.001466144,0.000739351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285197,"about_ca_system_score_gemma":0.0009600885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004876942,"about_ca_topic_score_gemma":0.003887295,"domain_scores_codex":[0.9930729,0.005036109,0.0001574072,0.001106624,0.0004974255,0.0001294702],"domain_scores_gemma":[0.960273,0.03624307,0.0009802226,0.001661454,0.0005844071,0.0002578103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0004262123,0.0004900255,0.04589104,0.004775995,0.0009006529,0.001969057,0.0147899,0.004024274,0.009474816,0.4837892,0.01936607,0.4141029],"study_design_scores_gemma":[0.0001479255,0.0004032433,0.1062554,0.001627708,0.0001628044,0.003579859,0.003093682,0.002371105,0.001806426,0.7451426,0.1353043,0.0001049714],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.1817122,0.3986152,0.1472273,0.04303367,0.002435236,0.0004831996,0.00146006,0.0005765355,0.2244566],"genre_scores_gemma":[0.8046228,0.0794346,0.085467,0.01228716,0.004568449,0.001303973,0.0005385544,0.0003377964,0.01143971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01467314,"threshold_uncertainty_score":0.07759994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02384016842379008,"score_gpt":0.3334056111107177,"score_spread":0.3095654426869276,"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."}}