{"id":"W23877756","doi":"","title":"SEMANTIC HETEROGENEITY OF GEODATA","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Semantic heterogeneity; Computer science; Semantic grid; Semantic computing; Spatial heterogeneity; Information retrieval; Semantic similarity; Semantic Web","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":[0.00006935706,0.00003953983,0.000061422,0.00002969966,0.0000180872,0.00005298775,0.0006589786,0.000009039731,0.0001972362],"category_scores_gemma":[0.00000628572,0.00003337321,0.00002352166,0.0001573636,0.00001229249,0.0004375142,0.0004627366,0.00001753647,0.0001262208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001965987,"about_ca_system_score_gemma":0.000001288491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001351903,"about_ca_topic_score_gemma":0.000005790641,"domain_scores_codex":[0.9995423,0.00001088243,0.00008720274,0.0001526482,0.0001131614,0.00009383431],"domain_scores_gemma":[0.9993402,0.000009339569,0.00002370074,0.0005876739,0.00001630869,0.00002278159],"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":[6.808846e-7,0.0005754617,0.007153135,0.0000951606,0.0001029926,0.00008576291,0.0001947316,0.00002874715,0.001538854,0.296198,0.1190922,0.5749342],"study_design_scores_gemma":[0.0004922813,0.00007091992,0.008881375,0.00002035807,0.00001289777,0.00001376218,0.00001964837,0.9070917,0.02715497,0.00133754,0.05457279,0.0003317203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01877838,0.0001597145,0.9504233,0.0007357313,0.0001883956,0.00005717297,0.00000391195,0.0001006441,0.02955275],"genre_scores_gemma":[0.9045823,0.00006359103,0.09114045,0.0003503812,0.00002592351,0.000001388943,0.000007112473,0.000002952395,0.003825884],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.907063,"threshold_uncertainty_score":0.2159598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0402596094850267,"score_gpt":0.236410829050712,"score_spread":0.1961512195656853,"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."}}