{"id":"W2125143886","doi":"10.5194/gmd-6-1591-2013","title":"An approach to computing direction relations between separated object groups","year":2013,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voronoi diagram; Object (grammar); Spatial relation; Gestalt psychology; Computation; Spatial intelligence; Object relations theory; Group (periodic table); Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Expression (computer science); Point (geometry); Mathematics; Pattern recognition (psychology); Theoretical computer science; Geometry; Algorithm; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005800861,0.0001656439,0.0001443928,0.0003548512,0.0006957204,0.0006120492,0.0004272558,0.0000705022,0.00003682536],"category_scores_gemma":[0.00002003974,0.0001685147,0.00003293736,0.00104224,0.00002975446,0.000889167,0.0001471608,0.0001156363,0.0004470684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001819832,"about_ca_system_score_gemma":0.0002180658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005053695,"about_ca_topic_score_gemma":0.00001171571,"domain_scores_codex":[0.9980558,0.00007566382,0.0004017603,0.0007065029,0.0004090181,0.0003512941],"domain_scores_gemma":[0.998955,0.00002495281,0.0000980777,0.0004367514,0.0002329032,0.000252302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001159954,0.0001354289,0.006151696,0.000009448253,0.00002052768,2.878166e-7,0.00698431,0.8065613,0.0009769402,0.004360473,0.002978025,0.1718204],"study_design_scores_gemma":[0.0001017533,0.000007058798,0.1094784,0.00000802949,0.00000267217,0.000002306482,0.00004232732,0.8891721,0.0002402328,0.00028478,0.0004466262,0.0002136986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1097311,0.000002991442,0.8843406,0.0001368629,0.0004147681,0.0004581368,0.000002943405,0.0003872701,0.004525329],"genre_scores_gemma":[0.5697773,1.98063e-7,0.4288923,0.00007381018,0.00001515868,0.00003751579,0.00007213662,0.000006317551,0.001125362],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4600461,"threshold_uncertainty_score":0.6871832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413297705041622,"score_gpt":0.2439449863339326,"score_spread":0.2198120092835164,"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."}}