{"id":"W2021189859","doi":"10.1890/09-0896.1","title":"Response of a boreal forest to canopy opening: assessing vertical and lateral tree growth with multi-temporal lidar data","year":2010,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Canopy; Taiga; Deciduous; Boreal; Lidar; Black spruce; Hardwood; Environmental science; Ecology; Tree canopy; Physical geography; Atmospheric sciences; Geography; Remote sensing; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003434475,0.0002025695,0.0001937033,0.0004809054,0.0004887801,0.000375157,0.0002854866,0.000202439,0.000275033],"category_scores_gemma":[0.0006686418,0.000125146,0.0001612088,0.0004930722,0.0001896362,0.0003163835,0.0002917598,0.0001947556,0.00005587208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008556262,"about_ca_system_score_gemma":0.000502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.20765,"about_ca_topic_score_gemma":0.4805216,"domain_scores_codex":[0.9998026,0.00001851411,0.000008175741,0.00005414056,0.00007073601,0.00004584209],"domain_scores_gemma":[0.9995529,0.00007445052,0.0000955783,0.00003059554,0.0001496676,0.00009681255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000140626,0.00007699971,0.9638514,0.0000312817,0.00003674374,0.0000797395,0.0004494965,0.001098125,0.01955707,0.00003866353,0.0001685227,0.01447129],"study_design_scores_gemma":[9.99432e-7,0.00001995013,0.9983929,0.000001545982,0.000004782642,0.00003336516,0.0001455416,0.0009873635,0.0003005363,0.000007078702,0.0001027914,0.000003079401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993671,0.00002726497,0.0001684839,0.000005516267,0.000001200003,0.000003532513,0.000173722,0.000007716585,0.0002455566],"genre_scores_gemma":[0.9990368,0.00002083532,0.0005080712,0.000006704678,0.000001234449,0.000005010665,0.0003422322,0.000001858526,0.00007729032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.20765,"threshold_uncertainty_score":0.4128827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02732794465233349,"score_gpt":0.2887980593581791,"score_spread":0.2614701147058456,"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."}}