{"id":"W2042181947","doi":"10.5539/jgg.v5n3p65","title":"Land Suitability Assessment for Crop Cultivation by Using Remote Sensing and GIS","year":2013,"lang":"en","type":"article","venue":"Journal of Geography and Geology","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agricultural land; Agriculture; Environmental science; West bengal; Land use; Agricultural engineering; Soil texture; Crop; Agroforestry; Geography; Remote sensing; Soil water; Soil science; Forestry; Civil engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005036987,0.00007516672,0.0001941535,0.00005388422,0.0001449527,0.00002857732,0.00004087797,0.00006685875,0.0001708747],"category_scores_gemma":[0.00005193537,0.0000545748,0.00008195885,0.00009812608,0.0002288071,0.0001775615,0.00003862165,0.00009364831,5.252405e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001683526,"about_ca_system_score_gemma":0.000005092626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003220152,"about_ca_topic_score_gemma":0.0002327181,"domain_scores_codex":[0.9992906,0.00007878429,0.0002481003,0.0001348367,0.00009813615,0.0001495288],"domain_scores_gemma":[0.9995305,0.0001261439,0.0001534189,0.00006923336,0.00004217972,0.00007852162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001749108,0.00002101891,0.9068741,0.000008824943,0.00003638,6.899107e-7,0.00009322227,0.00006840818,0.00141891,0.000001879807,0.0001314485,0.0913276],"study_design_scores_gemma":[0.0004849437,0.0001717128,0.9694207,0.000006114216,0.00006579643,0.00005542813,0.0001446598,0.01903342,0.00004909331,0.00929811,0.001182431,0.00008760545],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854077,0.0001524089,0.0131258,0.001033337,0.00004222466,0.00008647403,0.000002059183,0.000002701121,0.0001473247],"genre_scores_gemma":[0.9899302,0.00006688294,0.009828019,0.0001300501,0.00002589637,2.418072e-7,0.00000211715,0.00000254754,0.00001411416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09124,"threshold_uncertainty_score":0.4867927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00891993411901776,"score_gpt":0.2491861298661617,"score_spread":0.240266195747144,"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."}}