{"id":"W3007627489","doi":"10.3390/rs12040688","title":"Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Land cover; Remote sensing; Scale (ratio); Series (stratigraphy); Deforestation (computer science); Computer science; Time series; Cartography; Land use; Geography; Geology; Machine learning","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":[0.001658434,0.0002959819,0.0002689622,0.002878206,0.0003077604,0.0008851759,0.0006201891,0.0004041043,0.0006045339],"category_scores_gemma":[0.004002203,0.0003171294,0.0005394974,0.001709491,0.000337459,0.0008549087,0.0005987529,0.0004663749,0.0001365888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001589926,"about_ca_system_score_gemma":0.00074343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0737937,"about_ca_topic_score_gemma":0.1102575,"domain_scores_codex":[0.9996227,0.000115341,0.00002506756,0.0001421494,0.0000654399,0.00002929083],"domain_scores_gemma":[0.9992231,0.0003977302,0.0001810217,0.00004607316,0.0001061381,0.00004590386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001115886,0.0002363144,0.4615999,0.0001286739,0.0003514318,0.0002629641,0.001409757,0.3066194,0.002341803,0.01519374,0.003408398,0.208336],"study_design_scores_gemma":[0.000005409123,0.00001901275,0.09876562,0.00003480843,0.00004268276,0.00004446054,0.0002326575,0.8936763,0.0003620818,0.004933904,0.001858805,0.00002425966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8541036,0.0007146323,0.1361363,0.001177229,0.00002608761,0.0000943378,0.003429152,0.0004664983,0.003852243],"genre_scores_gemma":[0.9476436,0.0002655722,0.04899713,0.00005042207,0.00003065086,0.000056776,0.002344055,0.00006072256,0.0005510754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0737937,"threshold_uncertainty_score":0.1467283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01623664955688064,"score_gpt":0.1987208922275761,"score_spread":0.1824842426706954,"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."}}