{"id":"W2883822500","doi":"10.1016/j.jag.2018.07.010","title":"Using LiDAR waveform metrics to describe and identify successional stages of tropical dry forests","year":2018,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Inter-American Institute for Global Change Research; National Science Foundation","keywords":"Lidar; Ecological succession; Waveform; Vegetation (pathology); Metric (unit); Geography; Tropical and subtropical dry broadleaf forests; Ecology; Remote sensing; Environmental science; Computer science; Forestry; Biology; Engineering; Radar","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":[0.0005000301,0.0002606999,0.0001559055,0.002349353,0.0002206501,0.000812583,0.0001849369,0.0002115741,0.0003025404],"category_scores_gemma":[0.001416935,0.0001338836,0.0001949352,0.001073609,0.0001307667,0.0007201868,0.0002986532,0.0001750924,0.0001157172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000268045,"about_ca_system_score_gemma":0.0002713708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007322588,"about_ca_topic_score_gemma":0.01372796,"domain_scores_codex":[0.9999083,0.00001957638,0.00001246431,0.0000211451,0.00001586608,0.00002255954],"domain_scores_gemma":[0.9994903,0.0001735166,0.00013373,0.000026749,0.0001135053,0.00006218167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000378614,0.0001297658,0.8359686,0.00007664155,0.00009914768,0.0001347775,0.0004292108,0.01421952,0.02146644,0.0004385125,0.000444961,0.1262139],"study_design_scores_gemma":[0.00001588476,0.0001167796,0.8382639,0.00002785083,0.0000645269,0.0001633249,0.0008470081,0.1551518,0.003695471,0.0007907748,0.0008343402,0.00002830556],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965881,0.00007998208,0.002654709,0.00001061688,0.000003620584,0.000009718318,0.0002123537,0.00002470116,0.0004163271],"genre_scores_gemma":[0.9957355,0.00003984223,0.003676715,0.000003960654,0.000003002619,0.000005345707,0.0004137036,0.000006232433,0.0001156353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007322588,"threshold_uncertainty_score":0.01455992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03113430338553821,"score_gpt":0.2920578532435028,"score_spread":0.2609235498579646,"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."}}