{"id":"W2111098121","doi":"10.1037//0278-7393.26.4.900","title":"Updating geographical knowledge: Principles of coherence and inertia.","year":2000,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Learning Memory and Cognition","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Coherence (philosophical gambling strategy); Knowledge base; Geography; Latitude; Economic geography; Set (abstract data type); Regional science; Computer science; Artificial intelligence; Mathematics; Statistics; Geodesy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001909737,0.00008101265,0.0001439485,0.00009788405,0.00005893455,0.00001121355,0.00003245403,0.00007459912,0.0003278703],"category_scores_gemma":[0.00002157391,0.0000788722,0.00003231426,0.0000892845,0.0001078389,0.0001432981,0.000007022372,0.0002538516,0.000004306008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005252324,"about_ca_system_score_gemma":0.000004429972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.847255e-7,"about_ca_topic_score_gemma":6.169268e-7,"domain_scores_codex":[0.9993902,0.00007637816,0.0002987696,0.00008086682,0.00007320384,0.00008060912],"domain_scores_gemma":[0.9997139,0.00004589114,0.00009702705,0.00003075552,0.00005651612,0.00005590427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004195314,0.0002346344,0.003799031,0.00006186117,0.00009669732,0.00001234449,0.002012216,0.000393346,0.789936,0.00008134629,0.00009561041,0.2028574],"study_design_scores_gemma":[0.01199388,0.004767037,0.1895775,0.001416244,0.0002787736,0.002311184,0.007656097,0.009370402,0.7655153,0.001012281,0.005127642,0.000973707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845257,0.002677404,0.0001315651,0.00002575886,0.0001050893,0.0000489917,8.581017e-7,0.0000227567,0.01246184],"genre_scores_gemma":[0.9990699,0.0004920445,0.0002745162,0.00004794055,0.00007135505,0.000002881436,0.000005938451,0.000008707168,0.00002675821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2018837,"threshold_uncertainty_score":0.358995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986670720870629,"score_gpt":0.2985267284811773,"score_spread":0.278660021272471,"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."}}