{"id":"W6969450950","doi":"10.5683/sp3/lyikh3","title":"Leveraging machine learning and remote sensing to improve grassland inventory in British Columbia","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Grassland; Leverage (statistics); Support vector machine; Classifier (UML); Earth observation; Feature selection; Land cover","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004926031,0.0002716025,0.0001953652,0.001481528,0.0008811724,0.001011079,0.0007014782,0.0001745467,0.001668634],"category_scores_gemma":[0.001778735,0.0002047092,0.0001504954,0.002058833,0.0003236975,0.000328983,0.0005540211,0.0002994687,0.0003074574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009650531,"about_ca_system_score_gemma":0.007693816,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9800337,"about_ca_topic_score_gemma":0.9939434,"domain_scores_codex":[0.9997241,0.00003576877,0.00001454886,0.00006302618,0.00009219941,0.00007035895],"domain_scores_gemma":[0.9989856,0.0001536453,0.00007155888,0.00006956545,0.0005992575,0.0001203612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001876658,0.0001941677,0.7429718,0.0001198692,0.0001077175,0.0004794031,0.001080572,0.01700705,0.008811122,0.0005071908,0.006898366,0.2216351],"study_design_scores_gemma":[0.00002306031,0.00004354986,0.937488,0.00005397142,0.00004354911,0.00007210668,0.001809489,0.04849267,0.002645509,0.0001472902,0.009138481,0.00004227088],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9898742,0.000212966,0.001850448,0.0002099901,0.000007851025,0.00006630203,0.001677425,0.000259931,0.005840739],"genre_scores_gemma":[0.9862614,0.0001823319,0.007151962,0.00007549036,0.000003107826,0.00002851011,0.001915921,0.00003806962,0.0043432],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.9800337,"threshold_uncertainty_score":0.07001984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551325002563116,"score_gpt":0.2552601843900515,"score_spread":0.2397469343644203,"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."}}