{"id":"W2088763983","doi":"10.1139/x06-066","title":"Eliminating the effect of overlapping crowns from aerial inventory estimates","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Mathematics; Tree (set theory); Censoring (clinical trials); Crown (dentistry); Forest inventory; Forestry; Geography; Forest management; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001366251,0.00006452115,0.0001214643,0.0001073134,0.0003365821,0.00006622502,0.0003465108,0.00004333997,0.0002188874],"category_scores_gemma":[0.0004065716,0.00004373528,0.00006563409,0.0002787657,0.0005463567,0.00007765193,0.00003208688,0.0003167266,0.00003442389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334948,"about_ca_system_score_gemma":0.0001677372,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1885622,"about_ca_topic_score_gemma":0.1556967,"domain_scores_codex":[0.9988008,0.0001686333,0.0002351821,0.00009286962,0.0003977727,0.0003047347],"domain_scores_gemma":[0.9989827,0.0004800545,0.0001114164,0.0001829505,0.00004401858,0.0001989017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003146215,0.0000116093,0.9399586,0.00001181481,0.00002243699,0.00005272984,0.0005916456,0.007921584,0.02039816,0.0002989086,0.01770518,0.01299592],"study_design_scores_gemma":[0.0004024653,0.0002771747,0.970582,0.0001131567,0.00001953905,0.00004052337,0.0001569136,0.002503643,0.0109722,0.004130152,0.01070637,0.00009585638],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895687,0.0001339885,0.00007154771,0.0004374114,0.00008334305,0.0001074144,0.000005259825,0.000001895933,0.009590426],"genre_scores_gemma":[0.9993033,0.000001990135,0.0003511464,0.000007622014,0.0002123091,8.151513e-7,0.000002676418,0.000008545192,0.0001116239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03286547,"threshold_uncertainty_score":0.8597096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914501483865353,"score_gpt":0.2839909885135691,"score_spread":0.2648459736749156,"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."}}