{"id":"W2066781669","doi":"10.5194/isprsarchives-xxxix-b8-431-2012","title":"BIOMASS ESTIMATION USING VERTICAL FOREST STRUCTURE FROM SAR TOMOGRAGHY: A CASE STUDY IN CANADIAN BOREAL FOREST.","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"Taiga; Biomass (ecology); Forest structure; Environmental science; Remote sensing; Forestry; Physical geography; Ecology; Geology; Geography; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002982068,0.0004585832,0.0001563678,0.0009848216,0.000727413,0.0005193833,0.0004925526,0.0002578026,0.0007111612],"category_scores_gemma":[0.0006521981,0.000170003,0.0001858593,0.001592255,0.0003114678,0.0001901183,0.0001680981,0.0001565723,0.000108689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003267991,"about_ca_system_score_gemma":0.002223092,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9336232,"about_ca_topic_score_gemma":0.9760776,"domain_scores_codex":[0.999891,0.00001058014,0.0000044373,0.00001696519,0.00004460918,0.00003246564],"domain_scores_gemma":[0.999719,0.00006053322,0.00003513572,0.00001524694,0.0001434675,0.00002661886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004502363,0.0002478746,0.808806,0.0001945249,0.0001200447,0.002697091,0.000988598,0.05027818,0.02130081,0.000472222,0.001624363,0.1128201],"study_design_scores_gemma":[0.00002231788,0.00006946195,0.8939906,0.00002199539,0.00006139105,0.0003899568,0.001914008,0.0979125,0.003792798,0.0001249456,0.001666396,0.00003361935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966698,0.0001508887,0.001219561,0.0000503512,0.000002635873,0.00003118486,0.0005928156,0.00004210842,0.00124059],"genre_scores_gemma":[0.9950628,0.00008435746,0.00384921,0.000007166999,0.000001277487,0.000004211968,0.0004366254,0.00000630865,0.0005480028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06637681,"threshold_uncertainty_score":0.1335354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530676445559507,"score_gpt":0.2593159960323536,"score_spread":0.2440092315767586,"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."}}