{"id":"W3177690584","doi":"10.1016/j.heliyon.2021.e07522","title":"Mumbai's business landscape: A spatial analytical approach to urbanisation","year":2021,"lang":"en","type":"article","venue":"Heliyon","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Urbanization; Megacity; Geography; Urban sprawl; Economic geography; Metropolitan area; Tertiary sector of the economy; Regional science; Urban economics; Urban planning; Environmental planning; Economic growth; Economy; Economics; Civil engineering; Engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008710475,0.00008023188,0.0001092085,0.00001697194,0.00006644477,0.00005145694,0.0000965968,0.00005192217,0.002183984],"category_scores_gemma":[0.00001693835,0.00006497998,0.00002757536,0.0003382811,0.000003548638,0.0001078154,0.0001138396,0.00004232251,0.001765278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003789441,"about_ca_system_score_gemma":0.00001084795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002253675,"about_ca_topic_score_gemma":0.0008855011,"domain_scores_codex":[0.999199,0.00002903166,0.0001220108,0.0002587054,0.0002134066,0.0001778329],"domain_scores_gemma":[0.9996638,0.00001141956,0.00002200332,0.0001902879,0.00001411976,0.00009837229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001039962,0.0008699262,0.9514128,0.0007194161,0.00005640101,0.00009068204,0.002034483,0.01793122,0.006759803,0.0005203763,0.008072923,0.01142796],"study_design_scores_gemma":[0.0005378644,0.00003349786,0.9176847,0.00008192776,0.00003987939,0.00004415215,0.0002128929,0.02919445,0.001576927,0.00006058649,0.05018767,0.0003454805],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9479958,0.00008529462,0.003068661,0.0003884438,0.0001297951,0.00009230825,0.000004881517,0.00003640481,0.04819842],"genre_scores_gemma":[0.998311,0.000027384,0.0006048282,0.0004567498,0.0001468032,0.00001249005,0.00003728491,0.000008669123,0.000394759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05031525,"threshold_uncertainty_score":0.9990119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272792957912979,"score_gpt":0.2127952944560198,"score_spread":0.20006736487689,"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."}}