{"id":"W3202778618","doi":"10.3390/rs13193830","title":"Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Lidar; Hyperspectral imaging; Remote sensing; Ecological succession; Geography; Proxy (statistics); Environmental science; Tropical and subtropical dry broadleaf forests; Environmental resource management; Forestry; Computer science; Ecology; Machine learning","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":[0.0009686176,0.0004234969,0.0002073573,0.001373876,0.0001669301,0.0005982036,0.0003163063,0.0002912615,0.0005015855],"category_scores_gemma":[0.001209243,0.0001292607,0.0004148362,0.0007252739,0.000131783,0.0008781566,0.000559049,0.0003478707,0.0002007674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002207181,"about_ca_system_score_gemma":0.0003534876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004979025,"about_ca_topic_score_gemma":0.00988124,"domain_scores_codex":[0.9997656,0.00006680853,0.00001236883,0.00006925773,0.00005377701,0.00003220965],"domain_scores_gemma":[0.9996637,0.0001039365,0.00005568302,0.00003954174,0.0001023581,0.00003481309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003516802,0.0003842575,0.2172436,0.0001284891,0.0001190715,0.0001395937,0.0004593713,0.05750895,0.05525947,0.0008018247,0.0009980892,0.6666057],"study_design_scores_gemma":[0.00001752218,0.0001427425,0.3269234,0.00003117253,0.00006227716,0.0001371239,0.0005584289,0.6580303,0.01097713,0.000806828,0.002277877,0.00003527139],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561204,0.0003271503,0.04029423,0.000107422,0.00001585828,0.00004406955,0.0002959383,0.0003195988,0.002475346],"genre_scores_gemma":[0.9580243,0.0001311658,0.04069182,0.00003384622,0.00001215712,0.00001564923,0.0004286729,0.00001778288,0.0006447145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004979025,"threshold_uncertainty_score":0.009900093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008848756209957793,"score_gpt":0.2143999115972927,"score_spread":0.205551155387335,"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."}}