{"id":"W6920743485","doi":"10.6084/m9.figshare.23274583.v1","title":"Characterizing Tree Species in Northern Boreal Forests Using Multiple-Endmember Spectral Mixture Analysis and Multi-Temporal Satellite Imagery","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Endmember; Taiga; Satellite imagery; Context (archaeology); Boreal; Spectral signature; Shadow (psychology); Vegetation (pathology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009449017,0.0003834407,0.0002023643,0.001170649,0.0003017278,0.0006782548,0.000303849,0.0002185048,0.0003345751],"category_scores_gemma":[0.0007875673,0.0001527863,0.000353158,0.0005908121,0.0002067211,0.0008288667,0.0002474517,0.0001409715,0.0001326308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004188027,"about_ca_system_score_gemma":0.0003217609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04286166,"about_ca_topic_score_gemma":0.1684462,"domain_scores_codex":[0.9997539,0.00006004403,0.00001321102,0.00008625594,0.00005672334,0.00002988597],"domain_scores_gemma":[0.9997581,0.00006978276,0.00005033738,0.00003218018,0.00006416519,0.00002555291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007142871,0.0002660481,0.4890414,0.0001780063,0.0002611821,0.0001703234,0.0009879505,0.0520533,0.1118791,0.0007817097,0.0009427704,0.342724],"study_design_scores_gemma":[0.0000131881,0.00008074375,0.7164069,0.00002362396,0.000075647,0.0001401329,0.0005906703,0.272337,0.008663077,0.0004485128,0.001179927,0.0000405923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976465,0.0002410356,0.0216307,0.00003931761,0.000007616681,0.00002977482,0.000364685,0.0001870625,0.001034802],"genre_scores_gemma":[0.9580886,0.00009156715,0.04089363,0.00001304376,0.00000410939,0.0000111708,0.0005879151,0.00001719971,0.0002928526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04286166,"threshold_uncertainty_score":0.08522439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03252999088250202,"score_gpt":0.2451930574570548,"score_spread":0.2126630665745528,"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."}}