{"id":"W4311970570","doi":"10.3390/rs14246217","title":"Effects of Viewing Geometry on Multispectral Lidar-Based Needle-Leaved Tree Species Identification","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke; Cegep de Sainte Foy","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Normalization (sociology); Remote sensing; Multispectral image; Feature (linguistics); Computer science; Random forest; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001399836,0.0004857474,0.0003753411,0.0004781553,0.000387118,0.0007851578,0.0003925246,0.0002397264,0.0006209378],"category_scores_gemma":[0.00354215,0.0002525513,0.0003323355,0.000491592,0.0003827326,0.0005199235,0.0004516601,0.0002047639,0.0003254287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007357159,"about_ca_system_score_gemma":0.0004593164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03164206,"about_ca_topic_score_gemma":0.08224594,"domain_scores_codex":[0.9989206,0.0002907208,0.00005371152,0.000217787,0.0004027715,0.0001145203],"domain_scores_gemma":[0.9978958,0.001092879,0.0002507693,0.0001613905,0.0005041235,0.00009497156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00164132,0.0001894438,0.6800325,0.0001703456,0.0001777784,0.0003359634,0.0006246273,0.02073436,0.1917053,0.0001141971,0.0003100436,0.103964],"study_design_scores_gemma":[0.0000206077,0.0004443567,0.9161202,0.00001830027,0.00009714602,0.0004059805,0.0004241272,0.04337436,0.0382138,0.0001102207,0.0007265379,0.00004429653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951245,0.0001792572,0.003426372,0.00002421036,0.000008583283,0.000024038,0.0001768197,0.00005723065,0.0009790813],"genre_scores_gemma":[0.9935422,0.0001131796,0.005768302,0.0000209678,0.000004057517,0.000008926745,0.0003212862,0.00002130082,0.0001998514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03164206,"threshold_uncertainty_score":0.0629158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203668048246248,"score_gpt":0.2257138849364169,"score_spread":0.2136772044539545,"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."}}