{"id":"W2148937493","doi":"10.1109/fuzzy.2005.1452487","title":"Describing Topological Relationships in Words: Refinements","year":2005,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Counterintuitive; Object (grammar); Set (abstract data type); Computer science; Theoretical computer science; Natural language; Natural (archaeology); Spatial relation; Artificial intelligence; Topology (electrical circuits); Mathematics; Programming language; Geography","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":[0.003575835,0.001090938,0.0007525822,0.002801556,0.001182196,0.002805263,0.002059013,0.0009207886,0.01045129],"category_scores_gemma":[0.0125671,0.0009634055,0.002032256,0.003042496,0.00402506,0.01108611,0.002978691,0.002120618,0.00271527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001291304,"about_ca_system_score_gemma":0.001031282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003883558,"about_ca_topic_score_gemma":0.005728391,"domain_scores_codex":[0.9959461,0.001443532,0.0005635782,0.0008376318,0.001014335,0.0001949041],"domain_scores_gemma":[0.9934828,0.00325203,0.0003997916,0.00174028,0.00101024,0.0001148999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001020239,0.0000343345,0.0006626242,0.0004608184,0.0000496934,0.0003031268,0.004374415,0.01156076,0.004532327,0.8581921,0.003845684,0.115882],"study_design_scores_gemma":[0.0000272323,0.00005238491,0.0003160498,0.0001487961,0.00006899789,0.0003635907,0.001279859,0.05748174,0.005421497,0.8752132,0.05956302,0.00006357874],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01296841,0.0003571626,0.9749554,0.0004882353,0.000076071,0.0002862259,0.0008221613,0.0009346561,0.009111729],"genre_scores_gemma":[0.1598494,0.0005244695,0.8318257,0.0001852949,0.0001000085,0.0004589963,0.001795127,0.0003954247,0.004865612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01045129,"threshold_uncertainty_score":0.03496301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08285777074343353,"score_gpt":0.2658932788542005,"score_spread":0.183035508110767,"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."}}