{"id":"W4220798090","doi":"10.3390/land11030437","title":"Mapping Agricultural Lands: From Conventional to Regenerative","year":2022,"lang":"en","type":"article","venue":"Land","topic":"Organic Food and Agriculture","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canada Research Chairs; Canada Foundation for Innovation; Robert Wood Johnson Foundation","keywords":"Agriculture; Geography; Agricultural land; Context (archaeology); Land use; Interdependence; Environmental resource management; Diversity (politics); Regional science; Agroforestry; Economic geography; Environmental planning; Ecology; Political science; Sociology; Economics; Environmental science; Social science; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"category_scores_codex":[0.00005361325,0.0000860685,0.00009901702,0.000004428807,0.0003609925,0.00004179855,0.0001625327,0.00002554331,0.003528039],"category_scores_gemma":[0.000007752521,0.0000262887,0.00005440864,0.0002769528,0.000006809928,0.00004173072,0.000132665,0.0001072608,0.00007142864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002454133,"about_ca_system_score_gemma":0.000002945129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004758447,"about_ca_topic_score_gemma":0.001221945,"domain_scores_codex":[0.9992955,0.00005287432,0.00009757903,0.0002099603,0.0001924253,0.0001516432],"domain_scores_gemma":[0.9997995,0.00004400993,0.00003427329,0.00002398966,0.00002291089,0.00007526729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002864282,0.0001062734,0.04291626,0.000001466777,0.00004116357,0.00001330396,0.0007428847,0.00004482135,0.8718891,0.0002507863,0.08068401,0.003281309],"study_design_scores_gemma":[0.000163566,0.0001551813,0.7146839,0.00000542891,0.000004003355,0.00001027458,0.00197718,0.000004743018,0.002137546,0.000188231,0.2804992,0.0001706682],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989841,0.0001255993,0.000001367372,0.008377154,0.0002217071,0.0001154575,0.0004388166,0.00005137429,0.0008275651],"genre_scores_gemma":[0.9956829,0.000002167917,0.00005638052,0.0006721058,0.0006794908,0.00002857471,0.0007077249,4.186006e-7,0.002170212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8697515,"threshold_uncertainty_score":0.9973829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300959095482669,"score_gpt":0.1828027373654656,"score_spread":0.1697931464106389,"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."}}