{"id":"W2072474981","doi":"10.5558/tfc84866-6","title":"LiDAR and Weibull modeling of diameter and basal area","year":2008,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); Ministry of Natural Resources and Forestry; Queen's University","funders":"","keywords":"Weibull distribution; Basal area; Confidence interval; Lidar; Mathematics; Statistics; Environmental science; Range (aeronautics); Soil science; Forestry; Remote sensing; Materials science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00153362,0.0005385826,0.0003365048,0.0009893412,0.0002804823,0.0006439249,0.001032687,0.0005394747,0.0008760197],"category_scores_gemma":[0.006308163,0.0003001862,0.0005329826,0.0008029504,0.000504366,0.001168197,0.0003854078,0.0003167674,0.000507106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009881986,"about_ca_system_score_gemma":0.0004945372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03050468,"about_ca_topic_score_gemma":0.0257101,"domain_scores_codex":[0.9995003,0.0001143139,0.00002635617,0.0001326731,0.0001685687,0.00005781734],"domain_scores_gemma":[0.9984844,0.0008886192,0.0002248377,0.0001445055,0.0002166229,0.0000409813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000186425,0.00004437421,0.08599356,0.0000847242,0.00011405,0.0001511359,0.0003750301,0.7884327,0.007736203,0.006075071,0.000583182,0.1102236],"study_design_scores_gemma":[0.000004151848,0.00002746847,0.01726651,0.000009362601,0.00001455042,0.00009209456,0.00003910336,0.9777684,0.001033691,0.003006174,0.0007121027,0.00002640127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4079665,0.0007193125,0.5877926,0.0001694045,0.00002344454,0.00003571035,0.0004708867,0.0007212516,0.002100946],"genre_scores_gemma":[0.9772779,0.0002338802,0.0207107,0.00001733292,0.00001109574,0.00003061628,0.0003115242,0.0000339524,0.001372936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03050468,"threshold_uncertainty_score":0.06065422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803795693813857,"score_gpt":0.216443446870814,"score_spread":0.1984054899326754,"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."}}