{"id":"W4313482595","doi":"10.1007/978-981-19-4200-6_11","title":"The Use of Landsat TM Imagery for the Application of Rubber Tree Area and Stand Volume Predictive Models in Rubber Plantations in Selangor, Malaysia","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Forest Service; University of British Columbia","funders":"","keywords":"Thematic Mapper; Thematic map; Hevea brasiliensis; Tree (set theory); Natural rubber; Geography; Remote sensing; Agroforestry; Resource (disambiguation); Environmental science; Agriculture; Spatial analysis; Distribution (mathematics); Satellite imagery; Forestry; Cartography; Computer science; Mathematics","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":[0.0002199091,0.0003145066,0.0001127368,0.0003443461,0.0001177037,0.0006342442,0.000294416,0.0001934139,0.001274546],"category_scores_gemma":[0.0003114797,0.0002199876,0.0003278993,0.0005284265,0.0001251738,0.0004999061,0.0002075288,0.000255294,0.0005171376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003824499,"about_ca_system_score_gemma":0.000455013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02561233,"about_ca_topic_score_gemma":0.06829403,"domain_scores_codex":[0.9999532,0.000009277571,0.000002740933,0.00001023802,0.00001975521,0.000004860578],"domain_scores_gemma":[0.9999136,0.00004245901,0.000009351043,0.000007673013,0.00002246721,0.000004598605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007358488,0.00007671784,0.02639137,0.0004037019,0.00007305138,0.0004763285,0.0004580579,0.100826,0.01618449,0.003832661,0.01208828,0.8391157],"study_design_scores_gemma":[0.00001642044,0.0002255166,0.1869068,0.0004901832,0.0001705728,0.001182604,0.001527322,0.6466993,0.04477131,0.006009477,0.1118923,0.0001081287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7217496,0.01415157,0.1327563,0.002016989,0.0002136583,0.0001293348,0.004920372,0.001958272,0.1221039],"genre_scores_gemma":[0.8229235,0.01053894,0.1147436,0.0001469635,0.00003564623,0.0000402221,0.002462518,0.0002446289,0.04886395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02561233,"threshold_uncertainty_score":0.05092651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02574715604184618,"score_gpt":0.2194986391529644,"score_spread":0.1937514831111183,"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."}}