{"id":"W2770155217","doi":"10.1080/10106049.2017.1408704","title":"Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity","year":2017,"lang":"en","type":"article","venue":"Geocarto International","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"U.S. Geological Survey","keywords":"Normalized Difference Vegetation Index; Pixel; Land cover; Remote sensing; Vegetation (pathology); Geography; Land use; Computer science; Environmental science; Artificial intelligence; Climate change; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001066467,0.000414187,0.0002532933,0.002557683,0.0002758445,0.0007951395,0.0003828552,0.0002313127,0.00179383],"category_scores_gemma":[0.0002347132,0.0001909954,0.0004343561,0.001105751,0.0001433315,0.0002226215,0.0004517794,0.0001681561,0.000629126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003703393,"about_ca_system_score_gemma":0.0007376858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03405628,"about_ca_topic_score_gemma":0.07163891,"domain_scores_codex":[0.999903,0.000008444782,0.000006603331,0.00002234285,0.0000222181,0.00003756737],"domain_scores_gemma":[0.9999104,0.00001846528,0.00001033089,0.000008780838,0.00004469321,0.000007266018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004720976,0.0001783423,0.1512678,0.0004636274,0.0002816603,0.00238328,0.001282818,0.08107939,0.08888888,0.003241168,0.007604773,0.6628563],"study_design_scores_gemma":[0.00002362728,0.00008180228,0.4509167,0.000110929,0.0002261239,0.0008006535,0.002597756,0.4893567,0.03906007,0.001836181,0.01490413,0.00008521307],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8922861,0.0005110236,0.09484492,0.000150646,0.00002781488,0.0001319501,0.00271323,0.0008552757,0.008479109],"genre_scores_gemma":[0.9250948,0.0003184071,0.06428326,0.000045282,0.00001006808,0.00007301467,0.003938741,0.00009317493,0.006143286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03405628,"threshold_uncertainty_score":0.06771612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03114085379025866,"score_gpt":0.2752868675026731,"score_spread":0.2441460137124145,"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."}}