{"id":"W4200397974","doi":"10.3390/s22010035","title":"A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Ministry of Energy, Northern Development and Mines; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laser scanning; Identification (biology); Remote sensing; Scanner; Laser; Computer science; Environmental science; Artificial intelligence; Computer vision; Geography; Optics; Biology; Ecology; Physics","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":[0.00195468,0.0004721322,0.0003931018,0.002228817,0.0003615184,0.0007985184,0.0006572886,0.0007539575,0.001238079],"category_scores_gemma":[0.003060759,0.0003109874,0.0005921001,0.00150567,0.0002126399,0.001163481,0.0005172638,0.0003584513,0.0008862891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003693256,"about_ca_system_score_gemma":0.0003990666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002616332,"about_ca_topic_score_gemma":0.008454984,"domain_scores_codex":[0.9982567,0.0003070463,0.0001181987,0.0002564406,0.0009471456,0.0001145062],"domain_scores_gemma":[0.9968893,0.001030375,0.0002511572,0.0003045655,0.001418354,0.0001063183],"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":[0.001485176,0.000360863,0.2478807,0.0009721252,0.0006127247,0.0001437052,0.0005816942,0.01852884,0.1956645,0.001688573,0.005823589,0.5262575],"study_design_scores_gemma":[0.0002002631,0.001427765,0.6667387,0.0003239203,0.0006212011,0.001731046,0.001734858,0.1532212,0.1442707,0.002623584,0.02680727,0.0002995136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8868891,0.001267013,0.0905315,0.0002359455,0.0001406296,0.0003813134,0.005292059,0.002169602,0.01309287],"genre_scores_gemma":[0.7943991,0.0006673049,0.1973371,0.0001820983,0.00004422479,0.0002918043,0.005325967,0.0002181661,0.001534127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002616332,"threshold_uncertainty_score":0.01033741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0335524823753098,"score_gpt":0.2834094655099701,"score_spread":0.2498569831346603,"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."}}