{"id":"W7128539442","doi":"10.64903/1480-6800.22.1.1","title":"Assessing the Land Use Distribution of the Mass Rapid Transit Pedestrian Catchment Area (PCA) and its Geographical Context in the Kuala Lumpur Conurbation, Malaysia","year":2019,"lang":"","type":"article","venue":"Arab world geographer","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Land use; Distribution (mathematics); Context (archaeology); Land-use planning; Kuala lumpur; Conurbation; Catchment area; Urban planning","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":[0.000248533,0.0002137762,0.0001119263,0.00138204,0.0002767539,0.0008428347,0.0002575517,0.0001783177,0.0008896946],"category_scores_gemma":[0.000675154,0.0001383228,0.0001892175,0.002003262,0.0003749307,0.0004945116,0.0006375973,0.0001370895,0.0001622897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043344,"about_ca_system_score_gemma":0.0006292384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0522578,"about_ca_topic_score_gemma":0.08984265,"domain_scores_codex":[0.9997861,0.00005591414,0.00002923929,0.00004174896,0.00004455119,0.00004243861],"domain_scores_gemma":[0.9995835,0.00006277021,0.0001860364,0.00001716823,0.00009186226,0.00005863501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005102955,0.00002967769,0.9884822,0.00005241509,0.00002961035,0.0003128122,0.001685049,0.0009533875,0.0008305738,0.0001994739,0.0001891486,0.007184567],"study_design_scores_gemma":[6.938118e-7,0.00003219146,0.9947056,0.00001038552,0.0000103897,0.0001242544,0.003601167,0.0009654025,0.0001672068,0.00002786839,0.0003492429,0.000005496038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988054,0.0000445069,0.0001096487,0.00001454764,7.384328e-7,0.000009834106,0.0002917332,0.000002625718,0.0007209836],"genre_scores_gemma":[0.9993263,0.00004240333,0.0001548573,0.000002539916,6.79049e-7,0.000008927527,0.0001697298,7.745965e-7,0.0002936063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0522578,"threshold_uncertainty_score":0.1039073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02355578385248815,"score_gpt":0.2731510581717825,"score_spread":0.2495952743192943,"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."}}