{"id":"W4295414334","doi":"10.3390/rs14184522","title":"Mobile Laser Scanning for Estimating Tree Structural Attributes in a Temperate Hardwood Forest","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Health Research Institutes; Natural Sciences and Engineering Research Council of Canada; Mitacs; FPInnovations","keywords":"Hardwood; Diameter at breast height; Mean squared error; Crown (dentistry); Volume (thermodynamics); Laser scanning; Tree allometry; Allometry; Mathematics; Softwood; Forest inventory; Forestry; Tree (set theory); Statistics; Environmental science; Forest management; Geography; Ecology; Engineering; Laser; Pulp and paper industry; Materials science; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003538897,0.0001577032,0.0001883103,0.00006403369,0.0007259139,0.00006196721,0.0001290471,0.0000367682,0.00003689864],"category_scores_gemma":[0.00006831524,0.0001663577,0.00007658971,0.0003933633,0.00007055631,0.00009686228,0.0001932684,0.0002212237,0.00001916006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003474089,"about_ca_system_score_gemma":0.00002089363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008252672,"about_ca_topic_score_gemma":0.0003535552,"domain_scores_codex":[0.9986098,0.00007466476,0.0002756214,0.0003993862,0.0002401595,0.0004003195],"domain_scores_gemma":[0.9994391,0.0001032633,0.0001054968,0.000270627,0.00001154545,0.00006994675],"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":[0.00002422283,0.00001166462,0.001504409,0.00001166892,0.000008866511,0.00001250482,0.001369619,0.6502812,0.02544441,0.000008573867,0.0006445103,0.3206783],"study_design_scores_gemma":[0.0004008949,0.00006255399,0.001934884,0.00002316382,0.00001081023,0.00008038234,0.0004591457,0.9911858,0.002031503,0.0009444223,0.002658934,0.0002075309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779004,0.0000222939,0.01994633,0.0001889737,0.0001712973,0.0005204423,0.00001273688,0.00008224637,0.001155226],"genre_scores_gemma":[0.812057,6.419526e-7,0.1873971,0.0001098782,0.00007059592,3.753645e-7,0.00003458304,0.00002801036,0.0003018717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3409046,"threshold_uncertainty_score":0.6783872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551953550428173,"score_gpt":0.2565954992152227,"score_spread":0.2410759637109409,"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."}}