{"id":"W2022872731","doi":"10.1139/x10-024","title":"Comparisons between field- and LiDAR-based measures of stand structural complexity","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":192,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NASA Headquarters; Nature Conservancy; Washington State University","keywords":"Lidar; Canopy; Structural complexity; Percentile; Field (mathematics); Remote sensing; Environmental science; Forest ecology; Forest inventory; Forest structure; Geography; Ecology; Physical geography; Forest management; Ecosystem; Forestry; Mathematics; Statistics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000830955,0.00005911974,0.0001457898,0.0001438917,0.0002749921,0.00005341972,0.0002577576,0.00006138309,0.0002615374],"category_scores_gemma":[0.0002415932,0.00004947377,0.00003734152,0.0002142922,0.0008950256,0.00005956971,0.00002177483,0.000613283,0.00000616866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006225258,"about_ca_system_score_gemma":0.0003892941,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03483845,"about_ca_topic_score_gemma":0.3246135,"domain_scores_codex":[0.9989709,0.00008151185,0.0002106168,0.00009443379,0.000376262,0.0002663064],"domain_scores_gemma":[0.9988502,0.0002178646,0.00007876244,0.0001812285,0.000104293,0.0005676457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008968807,0.000003782563,0.9878106,0.000005618755,0.000007653893,0.000006670489,0.0001578646,0.00006968167,0.001522487,0.0002930972,0.0033113,0.006802269],"study_design_scores_gemma":[0.0001860857,0.0001229991,0.9796037,0.00001593816,0.000005981143,0.00001958188,0.0000781342,0.000241862,0.00215655,0.002281625,0.01523436,0.00005315627],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947474,0.00004719514,0.0003495351,0.0014743,0.0000436894,0.00008203105,0.00001727757,0.00000154195,0.00323705],"genre_scores_gemma":[0.9972237,0.000001268664,0.002652318,0.00001446777,0.00006270239,1.765605e-7,0.000001486327,0.00000548553,0.00003834471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.289775,"threshold_uncertainty_score":0.9715887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1186791273540472,"score_gpt":0.3441606639020126,"score_spread":0.2254815365479654,"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."}}