{"id":"W6906358479","doi":"10.17605/osf.io/fyrz8","title":"Regenerative-Conventional Agricultural Mapping","year":2022,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Ecology, Conservation, and Geographical Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Agriculture; Agricultural land; Agricultural productivity; Land use; Geographic information system","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":[0.001466735,0.0008147586,0.0006984017,0.004348621,0.000817132,0.003084485,0.002054948,0.0008921522,0.2987218],"category_scores_gemma":[0.008012448,0.0005766532,0.000717299,0.007127739,0.0005024112,0.002206469,0.003029166,0.000787559,0.1403338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005089641,"about_ca_system_score_gemma":0.001757104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004886143,"about_ca_topic_score_gemma":0.006982484,"domain_scores_codex":[0.9990876,0.00009621976,0.00006024047,0.0002356805,0.0004474637,0.00007281885],"domain_scores_gemma":[0.9965147,0.0006126898,0.0002012498,0.001487487,0.001009223,0.0001745901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.000243063,0.00009858835,0.005060572,0.0009113255,0.00006633671,0.000322652,0.0003978131,0.001919378,0.003160487,0.00982834,0.7768229,0.2011685],"study_design_scores_gemma":[0.00006336404,0.00002732072,0.006840411,0.0002369627,0.00002918828,0.0003952039,0.0002244431,0.002501511,0.002490315,0.00947486,0.9776782,0.00003817485],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01472624,0.001248395,0.1018286,0.001956677,0.001844604,0.0005988252,0.5127528,0.075734,0.2893097],"genre_scores_gemma":[0.1268774,0.002127179,0.1910079,0.0007970107,0.0009133625,0.001456269,0.4780166,0.03427848,0.1645258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2987218,"threshold_uncertainty_score":0.9993238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01472709516913424,"score_gpt":0.2204173296321145,"score_spread":0.2056902344629802,"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."}}