{"id":"W3133577567","doi":"10.1139/cjfr-2020-0506","title":"Mixtures of airborne lidar-based approaches improve predictions of forest structure","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Basal area; Context (archaeology); Lidar; Tree (set theory); Remote sensing; Mathematics; Geography; Forestry","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":[0.0005601888,0.00009225092,0.0002000627,0.0002382249,0.0001969789,0.00003682627,0.0003542767,0.0001077676,0.0003720374],"category_scores_gemma":[0.0003674305,0.00008000357,0.0001161211,0.0007274213,0.0007546577,0.00008634861,0.00003468918,0.0004983507,0.000005683254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002099031,"about_ca_system_score_gemma":0.001421229,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01145147,"about_ca_topic_score_gemma":0.1879435,"domain_scores_codex":[0.9984075,0.0001366152,0.0003712261,0.0001693227,0.0005555439,0.0003597681],"domain_scores_gemma":[0.9985635,0.0001417581,0.0001682949,0.0003750861,0.0002443278,0.0005070234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008842287,0.0002292092,0.8101848,0.0001738944,0.0001646338,0.0001965271,0.001698832,0.0876088,0.05346239,0.00275742,0.01562169,0.02781332],"study_design_scores_gemma":[0.0005838174,0.0003723417,0.9168455,0.00012019,0.00003824822,0.0001262998,0.0006653075,0.003034051,0.06172605,0.005476164,0.01086049,0.0001515653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924262,0.0002450506,0.0008510268,0.001178753,0.00009071725,0.0001445072,0.0001056656,0.000002505649,0.004955581],"genre_scores_gemma":[0.9969578,0.000007221695,0.002623358,0.00001712883,0.00009079918,9.46371e-7,0.00001376596,0.00001402868,0.0002749041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1764921,"threshold_uncertainty_score":0.9951314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03546972406133934,"score_gpt":0.2714467240481846,"score_spread":0.2359769999868452,"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."}}