{"id":"W2137918794","doi":"10.3390/f6113923","title":"Detecting Stems in Dense and Homogeneous Forest Using Single-Scan TLS","year":2015,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Chinese Academy of Sciences; Bureau of International Cooperation, Chinese Academy of Sciences; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Homogeneous; Merge (version control); Bamboo; Point cloud; Scale (ratio); Remote sensing; Computer science; Mathematics; Artificial intelligence; Biology; Ecology; Cartography; Geography","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":[0.0003329298,0.0003417968,0.0003428924,0.00206485,0.0002369604,0.0003169354,0.0003706476,0.0004458927,0.0007280033],"category_scores_gemma":[0.00052608,0.0002306263,0.0002551827,0.0009249419,0.0002518071,0.0006585647,0.0003859924,0.0001426345,0.0003741008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520894,"about_ca_system_score_gemma":0.0001906741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001322588,"about_ca_topic_score_gemma":0.005272783,"domain_scores_codex":[0.9998437,0.00002168914,0.00000873676,0.00005130131,0.00005591657,0.00001869956],"domain_scores_gemma":[0.9995111,0.00017871,0.00008564236,0.00004862733,0.0001297503,0.00004609998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004560609,0.0001576512,0.09932573,0.0004113861,0.00005630545,0.0006083388,0.0005454214,0.007793344,0.6264082,0.000242123,0.0005798996,0.2634156],"study_design_scores_gemma":[0.0001086684,0.0008283866,0.3723735,0.00007425481,0.0001395985,0.002508898,0.001567729,0.3789211,0.2385737,0.001476389,0.0032971,0.0001306363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9007434,0.0002277109,0.09632431,0.00003296223,0.00001291361,0.00007663327,0.0003872096,0.0009676803,0.001227244],"genre_scores_gemma":[0.8923766,0.0001187453,0.1065553,0.00002422206,0.000007114682,0.0000351066,0.0004234491,0.00002594646,0.0004334347],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00206485,"threshold_uncertainty_score":0.002629757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03920570863358924,"score_gpt":0.2505811550953921,"score_spread":0.2113754464618029,"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."}}