{"id":"W2113004090","doi":"10.1109/igarss.2007.4423814","title":"Forest structural information derived from multi-angular FIFEDOM (Frequent Image Frames Enhanced Digital Ortho-rectified Mapping) data","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Remote sensing; Taiga; Computer science; Pixel; Environmental science; Geography; Forestry; Artificial intelligence","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":[0.0003315589,0.0002790725,0.0002484347,0.001304016,0.0001882065,0.0004853786,0.0002764079,0.0001955674,0.0008315321],"category_scores_gemma":[0.001001867,0.0001663449,0.0003495679,0.001092042,0.0001766656,0.0005577022,0.0002677936,0.000196416,0.0001868985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005442564,"about_ca_system_score_gemma":0.0005364405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05381472,"about_ca_topic_score_gemma":0.1404097,"domain_scores_codex":[0.9997892,0.00002239788,0.00001381086,0.00003988965,0.00008985928,0.0000448287],"domain_scores_gemma":[0.9997223,0.00004988502,0.00005145775,0.00005677165,0.0001076058,0.00001198202],"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":[0.0006504287,0.000240057,0.2887602,0.0003234584,0.0001594155,0.0003505737,0.0003767972,0.2225885,0.08462121,0.001185814,0.001783506,0.3989601],"study_design_scores_gemma":[0.00005635516,0.0001297115,0.4562587,0.00005080245,0.0001082575,0.000278197,0.0004448439,0.5052228,0.03319224,0.0005450053,0.003615505,0.00009760867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976662,0.0001065905,0.01792885,0.00004431662,0.00001301167,0.0000314902,0.002060016,0.0002999324,0.002853607],"genre_scores_gemma":[0.9678137,0.00009916928,0.0287889,0.000009413415,0.000004458404,0.000009457362,0.00277543,0.0000263043,0.0004731697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05381472,"threshold_uncertainty_score":0.107003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01750599725101195,"score_gpt":0.2530534405817236,"score_spread":0.2355474433307116,"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."}}