{"id":"W3048942692","doi":"10.1002/rob.21980","title":"Automatic three‐dimensional mapping for tree diameter measurements in inventory operations","year":2020,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Recherche Industrielle du Québec; Université Laval; McGill University","funders":"Mitacs","keywords":"Forest inventory; Context (archaeology); Tree (set theory); Computer science; Scale (ratio); Automation; Robotics; Forest management; Artificial intelligence; Robot; Data mining; Machine learning; Forestry; Geography; Mathematics; Cartography; Engineering","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":[0.000704054,0.001144217,0.000704503,0.001784171,0.0004435265,0.001043593,0.001555728,0.0009117372,0.001636181],"category_scores_gemma":[0.002448784,0.0003235802,0.0007104672,0.001452641,0.0002698443,0.001050026,0.0008658775,0.0008318143,0.001731482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000476058,"about_ca_system_score_gemma":0.0007537567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0103248,"about_ca_topic_score_gemma":0.02720295,"domain_scores_codex":[0.9990306,0.0001648892,0.00006185882,0.0003765971,0.0002507207,0.0001154801],"domain_scores_gemma":[0.9985682,0.0004513813,0.000169971,0.0003814673,0.000340488,0.00008852823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001071536,0.001074556,0.124465,0.001196143,0.0004968896,0.0006608583,0.0005334489,0.1663609,0.04352545,0.001994575,0.07807027,0.5805505],"study_design_scores_gemma":[0.00006980148,0.0001146015,0.06388932,0.0001083516,0.00004197559,0.0003341214,0.0003810719,0.8910888,0.02527541,0.001825624,0.01679197,0.00007894496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5957686,0.002470535,0.311829,0.0005917763,0.0006502994,0.0004240705,0.04674952,0.03454698,0.00696907],"genre_scores_gemma":[0.6791723,0.000396165,0.2579715,0.0001341154,0.00007479884,0.0002102592,0.05957311,0.0004995654,0.001968252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0103248,"threshold_uncertainty_score":0.02052939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06902470118011261,"score_gpt":0.2667918385884821,"score_spread":0.1977671374083695,"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."}}