{"id":"W4404048892","doi":"10.1101/2024.11.03.621732","title":"Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Canopy; Temperate forest; Temperate rainforest; Temperate climate; Environmental science; Imaging spectroscopy; Spectroscopy; Tree canopy; Agroforestry; Remote sensing; Forestry; Geography; Ecology; Biology; Hyperspectral imaging; Ecosystem; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000430289,0.0003327797,0.0002145506,0.0006067985,0.0002451331,0.0002890906,0.0001793639,0.000154791,0.0002907726],"category_scores_gemma":[0.00027832,0.0001425647,0.0002286588,0.0004759659,0.0001174674,0.0002306778,0.0001832629,0.0001106949,0.00006978134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001942265,"about_ca_system_score_gemma":0.0001002202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0136927,"about_ca_topic_score_gemma":0.02251398,"domain_scores_codex":[0.9998785,0.00002550649,0.000005585309,0.00005320381,0.00002120683,0.00001591132],"domain_scores_gemma":[0.999818,0.00006047073,0.00004380182,0.00001642797,0.00003772516,0.00002348273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004184189,0.0001300458,0.729925,0.00005742187,0.0001431846,0.0001464039,0.0003140723,0.004950977,0.2340125,0.00006515159,0.0001009482,0.029736],"study_design_scores_gemma":[0.000005575987,0.00007428746,0.9809585,0.000003692919,0.00003411715,0.00007627393,0.0001050132,0.01581533,0.002794338,0.00003524324,0.00009070028,0.000006807531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986145,0.0000369013,0.001159243,0.000002424841,3.795536e-7,0.000002843279,0.00006766681,0.00001376706,0.000102238],"genre_scores_gemma":[0.997418,0.00001915934,0.002385429,0.000003468049,0.000001223802,0.00000392399,0.0001304974,0.000002854922,0.00003540508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0136927,"threshold_uncertainty_score":0.02722603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144463119030765,"score_gpt":0.203401684391787,"score_spread":0.1919570532014794,"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."}}