{"id":"W2050599442","doi":"10.1023/b:gein.0000034819.57376.92","title":"Development of a Temporal Extension to Query Travel Behavior Time Paths Using an Object-Oriented GIS","year":2004,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Université de Montréal","funders":"","keywords":"Computer science; Chaining; Temporal database; Set (abstract data type); Extension (predicate logic); Operator (biology); Data mining; Path (computing); Data model (GIS); Object (grammar); Event (particle physics); Spatiotemporal database; Spatial query; Query language; Information retrieval; Database; Database design; View; Artificial intelligence; Web query classification; Web search query; Programming language; Search engine","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":[0.0003546863,0.0001503134,0.0001975105,0.0002148408,0.0001369082,0.0001070421,0.0005681421,0.00003696329,0.00002018019],"category_scores_gemma":[0.00001256372,0.0001352608,0.00004044109,0.0004167421,0.00002263278,0.001720746,0.0004355713,0.00006142302,0.0002121409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006623418,"about_ca_system_score_gemma":0.0001535737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005842178,"about_ca_topic_score_gemma":0.000008310077,"domain_scores_codex":[0.9985477,0.00001148508,0.0005200049,0.0002021861,0.0004090877,0.0003094948],"domain_scores_gemma":[0.9990606,0.000006824004,0.0001345502,0.0005670806,0.00007819751,0.0001527625],"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":[0.00005904404,0.002308642,0.0002680217,0.0003633396,0.0001152405,0.0001609464,0.08881468,0.001955311,0.02097144,0.01925405,0.000924801,0.8648045],"study_design_scores_gemma":[0.006780005,0.001701722,0.05484836,0.001264702,0.0001929703,0.000207456,0.007556866,0.8355055,0.06410474,0.001322267,0.02285763,0.003657747],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.414026,0.000002762924,0.5847849,0.00003805929,0.0001215442,0.000331544,0.000008312752,0.00008207915,0.0006048781],"genre_scores_gemma":[0.1348328,5.258444e-7,0.8647825,0.0001910897,0.00001668849,0.00001625433,0.00005843219,0.000007764446,0.00009398867],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8611467,"threshold_uncertainty_score":0.5515776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02474059615807743,"score_gpt":0.2568433149159489,"score_spread":0.2321027187578714,"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."}}