{"id":"W3042667820","doi":"","title":"Hyperspectral remote sensing algorithms for retrieving forest chlorophyll content","year":2007,"lang":"en","type":"article","venue":"TSpace","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nanjing University; Natural Resources Canada; University of Toronto","keywords":"Hyperspectral imaging; Remote sensing; Computer science; Content (measure theory); Algorithm; Environmental science; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0004394886,0.000520089,0.0003045146,0.0008745918,0.0002382008,0.0005356815,0.0005049449,0.0003942554,0.001287714],"category_scores_gemma":[0.0007482683,0.0002510853,0.0005026883,0.0008102757,0.0001849621,0.0006501994,0.0003895831,0.0004018038,0.0006989504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005413471,"about_ca_system_score_gemma":0.0004881169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003077869,"about_ca_topic_score_gemma":0.004099789,"domain_scores_codex":[0.9998343,0.00003087691,0.00001100103,0.00003386043,0.00007450124,0.0000154851],"domain_scores_gemma":[0.9998208,0.00004876604,0.0000238741,0.0000341655,0.00006736378,0.000005157402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009969591,0.0001378719,0.003017326,0.0001786714,0.0001219847,0.00006772597,0.00009234797,0.2629049,0.09885909,0.01098703,0.002827167,0.6207061],"study_design_scores_gemma":[0.00001052738,0.00002322899,0.002422693,0.000007753862,0.00001616873,0.00003545388,0.00001979346,0.9804724,0.01085634,0.003514948,0.002606,0.00001471057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02965142,0.0002872318,0.9659668,0.00005453341,0.00002124111,0.00008085761,0.0002735119,0.001374358,0.002290084],"genre_scores_gemma":[0.1384758,0.0003230667,0.8576589,0.00005718095,0.00002029458,0.0001944444,0.0006886324,0.00009731224,0.002484428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003077869,"threshold_uncertainty_score":0.006119907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03788610607822253,"score_gpt":0.2895716017440851,"score_spread":0.2516854956658626,"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."}}