{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001655894,0.0001584201,0.0001205858,0.00333799,0.0007648992,0.002313975,0.0005131965,0.0003566487,0.001004647],"category_scores_gemma":[0.004771686,0.0001311163,0.0001352359,0.005467975,0.001880912,0.002390683,0.002351888,0.000277568,0.000136334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009108728,"about_ca_system_score_gemma":0.0006048451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01273419,"about_ca_topic_score_gemma":0.02826227,"domain_scores_codex":[0.9991067,0.0003549158,0.00006164728,0.0002146602,0.0001779277,0.00008404675],"domain_scores_gemma":[0.9980127,0.0008623769,0.0004320211,0.0002469148,0.000365289,0.00008079254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000250525,0.00007465226,0.5471259,0.0006624015,0.00009291908,0.000672657,0.124771,0.006124589,0.01137015,0.04605599,0.00291988,0.2598794],"study_design_scores_gemma":[0.00001237147,0.00009975595,0.6986133,0.0003313504,0.00007639303,0.0005224062,0.2175617,0.009011443,0.003137949,0.03636613,0.03421174,0.00005539888],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9685832,0.000724088,0.01522072,0.000587744,0.000009820843,0.00006687995,0.0006707934,0.00005777356,0.014079],"genre_scores_gemma":[0.9907705,0.0001941652,0.008419379,0.00002543528,0.000005838789,0.00002294864,0.0001871375,0.000009815924,0.0003648187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01273419,"threshold_uncertainty_score":0.02532011,"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."}}