{"id":"W2015414733","doi":"10.1002/asi.22648","title":"Modeling geographic, temporal, and proximity contexts for improving geotemporal search","year":2012,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Query expansion; Web search query; Probabilistic logic; Context (archaeology); Data mining; Search engine; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002602289,0.00006990897,0.0001413219,0.0002545545,0.0004911399,0.0002450936,0.0008228158,0.00002761551,7.436622e-8],"category_scores_gemma":[0.0001573385,0.00004576323,0.00008656055,0.001103296,0.000783498,0.007232421,0.0004598468,0.0001252848,1.668098e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003008806,"about_ca_system_score_gemma":0.000112617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001708858,"about_ca_topic_score_gemma":4.750959e-7,"domain_scores_codex":[0.9990312,0.00000603855,0.0002760452,0.0000848223,0.0003170526,0.0002848607],"domain_scores_gemma":[0.998751,0.00003240825,0.0004004619,0.0001915288,0.0005606673,0.00006390408],"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":[0.00001576445,0.00002845913,0.009010588,0.00007634699,0.00004040092,5.836494e-8,0.001502611,0.00006751496,0.0007829489,0.04645456,0.0005788367,0.9414419],"study_design_scores_gemma":[0.0008340651,0.0004396421,0.001590276,0.00002010522,0.00002860482,0.00006339657,0.005174265,0.9715893,0.000833418,0.004515191,0.0147143,0.0001974782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3663173,0.00009872133,0.6270216,0.006075225,0.0001415302,0.0003022994,0.000005396496,0.00002556412,0.00001231796],"genre_scores_gemma":[0.9072386,0.00004961323,0.09212724,0.0005307204,0.0000380736,0.000009076815,4.900778e-7,0.000001793682,0.000004418163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9715217,"threshold_uncertainty_score":0.5243331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190929325820931,"score_gpt":0.2779477077519878,"score_spread":0.2588547751698947,"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."}}