{"id":"W2104215496","doi":"10.6000/1927-5129.2014.10.02","title":"Socio-Agricultural Correlation and Regionalization: A Case of the Districts of Pakistan","year":2014,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geography; Indus; Agriculture; Distribution (mathematics); Socioeconomics; Population; Agricultural economics; Demography; Economics","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":[0.0004046302,0.0001390679,0.0001846984,0.001570047,0.001377969,0.00138002,0.0003310706,0.0002754298,0.002400957],"category_scores_gemma":[0.001685291,0.0001536008,0.0003067129,0.003475313,0.001092929,0.0004552471,0.001098771,0.0004046977,0.000181582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001584706,"about_ca_system_score_gemma":0.00132867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1143529,"about_ca_topic_score_gemma":0.1594046,"domain_scores_codex":[0.9994412,0.0002145137,0.00001651997,0.00006922464,0.00007057759,0.000187938],"domain_scores_gemma":[0.9988887,0.000379031,0.0002650723,0.00008677095,0.0002100657,0.0001704017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009254221,0.0001106917,0.9695106,0.00003358994,0.00005741908,0.006482913,0.008877615,0.001704927,0.0003097907,0.003035376,0.0006142344,0.009170285],"study_design_scores_gemma":[0.000009014491,0.00006972296,0.9532601,0.00002149175,0.00004041672,0.001599147,0.03908425,0.003089995,0.0001324767,0.0006956868,0.001980082,0.00001750222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976641,0.00004471763,0.0001532484,0.00009208967,0.00000158579,0.00001243349,0.0001420138,0.000002196909,0.001887681],"genre_scores_gemma":[0.9996043,0.00004358858,0.0001078212,0.000006058174,0.000001537211,0.00000296864,0.00005504887,9.640282e-7,0.0001776564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1143529,"threshold_uncertainty_score":0.2273746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008414364674967287,"score_gpt":0.2217635180006007,"score_spread":0.2133491533256334,"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."}}