{"id":"W1604580104","doi":"10.1080/07038992.2014.943392","title":"Estimating Canopy Height of Deciduous Forests at a Regional Scale with Leaf-Off, Low Point Density LiDAR","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Canopy; Deciduous; Scale (ratio); Geography; Tree canopy; Environmental science; Remote sensing; Ecosystem; Physical geography; Forest ecology; Forest inventory; Field (mathematics); Forestry; Ecology; Cartography; Forest management; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004689191,0.0002756323,0.0002081906,0.0007387935,0.0002306978,0.0003648086,0.000365575,0.0001889293,0.0003235049],"category_scores_gemma":[0.0008454536,0.0001830654,0.0002117466,0.0006094408,0.00008580717,0.0005137572,0.000229552,0.0001829077,0.0001464529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003754323,"about_ca_system_score_gemma":0.0003044812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01947041,"about_ca_topic_score_gemma":0.04493878,"domain_scores_codex":[0.9998379,0.00003776709,0.000009314229,0.00006387522,0.00003452922,0.00001656079],"domain_scores_gemma":[0.9994984,0.0002089179,0.00007017275,0.00005556057,0.0001385945,0.00002841228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001191879,0.0002206635,0.7960981,0.00007930562,0.0001665139,0.0001528746,0.0001894094,0.09655315,0.01731744,0.0002046211,0.001066859,0.08783188],"study_design_scores_gemma":[0.00001677276,0.00006047441,0.4995213,0.00002204502,0.00006166417,0.0001334588,0.0003088267,0.4918984,0.006817976,0.0003127103,0.0008212959,0.00002514722],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901109,0.00008445339,0.008462778,0.00002034009,0.000003259872,0.00001162765,0.0006865456,0.0001360459,0.0004838786],"genre_scores_gemma":[0.985435,0.0000299518,0.01366123,0.000009268677,0.000002028306,0.000008601721,0.0007536861,0.000008876783,0.00009140783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01947041,"threshold_uncertainty_score":0.03871417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007542361810556171,"score_gpt":0.2019727790590793,"score_spread":0.1944304172485231,"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."}}