{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005085958,0.0002526038,0.0001786804,0.000989427,0.0002118741,0.0002904205,0.0002564508,0.0002037539,0.0003445548],"category_scores_gemma":[0.000604648,0.000103964,0.0001522676,0.0006476062,0.0001189592,0.0003115827,0.0002314144,0.0001146912,0.0001671919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002600662,"about_ca_system_score_gemma":0.0002700118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006975475,"about_ca_topic_score_gemma":0.01568049,"domain_scores_codex":[0.9997982,0.00003744933,0.000007198209,0.00004663685,0.00009975702,0.00001080064],"domain_scores_gemma":[0.9997521,0.00009049586,0.00004531083,0.00001957997,0.00007670117,0.00001575736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004012116,0.0001770894,0.4916988,0.0001835978,0.00007389146,0.0001819227,0.0005185377,0.04920576,0.2376328,0.0003384651,0.000287172,0.2193007],"study_design_scores_gemma":[0.00003070779,0.0006277524,0.6058906,0.00002555136,0.00006768459,0.000229813,0.000526802,0.3684594,0.02284498,0.0004010112,0.0008494252,0.00004628392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872987,0.000137448,0.01195082,0.000007975658,0.000002238219,0.00001513107,0.0001476384,0.00007035645,0.0003697241],"genre_scores_gemma":[0.9841834,0.000058003,0.01539163,0.000005773883,0.000002765688,0.00001703603,0.0001935538,0.000006167522,0.0001416539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006975475,"threshold_uncertainty_score":0.01386976,"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."}}