{"id":"W1972344169","doi":"10.5539/mas.v3n4p62","title":"New Approaches in Estimating Rubberwood Standing Volume Using Airborne Hyperspectral Sensing","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Volume (thermodynamics); Remote sensing; Natural rubber; Environmental science; Tree (set theory); Diameter at breast height; Mathematics; Forestry; Geography; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006283983,0.0001847341,0.0001813566,0.0001232452,0.0005023377,0.0001665295,0.0003596889,0.0000553954,0.00002929764],"category_scores_gemma":[0.00002599192,0.0001885301,0.00003366559,0.001156393,0.0003402492,0.0002981425,0.0001127073,0.000219739,0.00006547628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006016562,"about_ca_system_score_gemma":0.00008898097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003281536,"about_ca_topic_score_gemma":0.00003252577,"domain_scores_codex":[0.99781,0.00001514888,0.0002581319,0.0007161185,0.0005682198,0.0006323927],"domain_scores_gemma":[0.9992802,0.00001932016,0.00009345755,0.0003984717,0.000004981483,0.0002035414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006564322,0.00003240197,0.0002766264,0.000001489401,0.000001012243,0.000003289379,0.002336292,0.1676935,0.623639,0.0008768417,0.00002447905,0.2051085],"study_design_scores_gemma":[0.0001811436,0.00001291746,0.005851062,0.00001438253,0.000005595059,0.00002325357,0.0002149086,0.9729629,0.007781097,0.01268923,0.00002947096,0.0002340564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4619421,0.000008210769,0.5188681,0.0002785971,0.00003711303,0.0001784088,3.906264e-7,0.00007161961,0.01861546],"genre_scores_gemma":[0.6436831,2.847619e-7,0.3561296,0.00006809973,0.00004343221,2.527352e-7,5.809706e-7,0.000008681832,0.00006590319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8052694,"threshold_uncertainty_score":0.7688035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03845224984452375,"score_gpt":0.2518541639649299,"score_spread":0.2134019141204062,"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."}}