{"id":"W1966071940","doi":"10.1109/digitalheritage.2013.6743809","title":"Laser-scanned tree stem filtering for forest inventories measurements","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Point cloud; Laser scanning; Tree (set theory); Forest inventory; Remote sensing; Computer science; Forest management; Geography; Environmental science; Artificial intelligence; Forestry; Laser; Mathematics; Optics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004257368,0.0004578088,0.0004852518,0.001524461,0.0003190271,0.0005800693,0.0004803481,0.000502706,0.004827811],"category_scores_gemma":[0.001256227,0.0002977293,0.0004012025,0.001621941,0.0001274279,0.000534842,0.0002852347,0.0002951771,0.001781601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745355,"about_ca_system_score_gemma":0.0004504012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003518346,"about_ca_topic_score_gemma":0.006912733,"domain_scores_codex":[0.9996046,0.00006378334,0.00002661201,0.00008527936,0.0001792471,0.00004051799],"domain_scores_gemma":[0.9993538,0.0002687763,0.00007029373,0.0001245314,0.0001553348,0.00002726329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004222866,0.0002136382,0.02706623,0.0004069992,0.0001085102,0.0002173662,0.0001924928,0.02578675,0.2743621,0.002337448,0.005085528,0.6638007],"study_design_scores_gemma":[0.00004887477,0.000220116,0.1337136,0.00007772671,0.0001000432,0.0005926557,0.0001653418,0.6565014,0.1845632,0.003482495,0.02044342,0.00009114956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1184366,0.0003661676,0.8707528,0.00005712708,0.00005947175,0.0001474601,0.002361593,0.004994811,0.002824064],"genre_scores_gemma":[0.4162153,0.0002982436,0.5780146,0.00006238167,0.00004115866,0.000208854,0.003184993,0.0002457757,0.001728661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004827811,"threshold_uncertainty_score":0.01615059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06399753670354118,"score_gpt":0.2602798919547961,"score_spread":0.1962823552512549,"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."}}