{"id":"W2591930544","doi":"10.5555/arwg.13.2.g240w6g8ww015711","title":"Delineating catchment areas of selected KTM komuter stations in the kuala lumpur conurbation using a gis-based approach","year":2010,"lang":"en","type":"article","venue":"Arab world geographer","topic":"Urban Transport Systems Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conurbation; Kuala lumpur; Catchment area; Park and ride; Geographic information system; Geography; Public transport; Drainage basin; Transport engineering; Environmental resource management; Environmental planning; Environmental science; Engineering; Cartography; Business; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002016763,0.0002161509,0.0002143927,0.002628164,0.0004762328,0.0009930123,0.0003164402,0.0001916771,0.0022751],"category_scores_gemma":[0.0007936009,0.0001955999,0.0001780503,0.005030509,0.0002918627,0.0003046891,0.0007713785,0.0001162862,0.0002976861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521401,"about_ca_system_score_gemma":0.001414829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1107106,"about_ca_topic_score_gemma":0.1580121,"domain_scores_codex":[0.9998366,0.000042834,0.00001855152,0.000026514,0.00003654389,0.00003893008],"domain_scores_gemma":[0.9997945,0.00006438533,0.00004822818,0.00001207544,0.00005760564,0.00002317383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005417113,0.000264132,0.6943941,0.0007163722,0.0001498954,0.002714063,0.01473446,0.05749049,0.01347961,0.005243208,0.002813702,0.2074582],"study_design_scores_gemma":[0.000018066,0.00008980211,0.9183149,0.00007249255,0.00007349103,0.0003592109,0.01647444,0.05203523,0.003634274,0.0006107371,0.008259399,0.00005795882],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947333,0.00006640325,0.001104946,0.00002503153,0.000001143178,0.0001150422,0.0008832738,0.00002995473,0.00304074],"genre_scores_gemma":[0.9916944,0.0001211203,0.005105669,0.000004665917,0.0000015481,0.000165613,0.0009722784,0.000008377619,0.001926226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1107106,"threshold_uncertainty_score":0.2201324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104811311441548,"score_gpt":0.2211494344006474,"score_spread":0.2101013212862319,"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."}}