{"id":"W1608928399","doi":"10.1007/3-540-45486-1_6","title":"Using Object Influence Areas to Quantitatively Deal with Neighborhood and Perception in Route Descriptions","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Perception; Object (grammar); Orientation (vector space); Space (punctuation); Object-orientation; Natural (archaeology); Artificial intelligence; Natural language; Human–computer interaction; Theoretical computer science; Object-oriented programming; Geography; Programming language; Mathematics; Epistemology","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":[0.001209349,0.0008256249,0.0006563463,0.002932603,0.0003928693,0.001350817,0.0008124828,0.0006450923,0.000888581],"category_scores_gemma":[0.005105003,0.000546724,0.0006790422,0.002250009,0.000688531,0.002171803,0.0009488622,0.0004762806,0.0002178161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006835792,"about_ca_system_score_gemma":0.0004258339,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01111181,"about_ca_topic_score_gemma":0.01413212,"domain_scores_codex":[0.9994111,0.000163276,0.00003616162,0.0001339445,0.0002101301,0.00004527757],"domain_scores_gemma":[0.9978936,0.001286163,0.0002085276,0.0002018547,0.000340889,0.00006890183],"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":[0.0004305164,0.0002162713,0.03540825,0.0003172592,0.0004218735,0.0002000503,0.001085503,0.3545613,0.05301719,0.02111908,0.001678968,0.5315436],"study_design_scores_gemma":[0.00001124178,0.00004815976,0.009089997,0.00001033559,0.00008836218,0.0001212785,0.0001088312,0.971709,0.01029342,0.007452275,0.001035602,0.00003147278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.158076,0.0004836241,0.8382846,0.00005037476,0.0000333088,0.00005527,0.0002190401,0.0009137472,0.001884047],"genre_scores_gemma":[0.7411846,0.0002264776,0.256874,0.00001670356,0.00003910967,0.00007405909,0.0003131239,0.0002355985,0.001036257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01111181,"threshold_uncertainty_score":0.02209431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0369220270604123,"score_gpt":0.2966343836070376,"score_spread":0.2597123565466253,"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."}}