{"id":"W2889267302","doi":"10.3390/f9090540","title":"Influence of Natural and Anthropogenic Linear Canopy Openings on Forest Structural Patterns Investigated Using LiDAR","year":2018,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; Université du Québec à Montréal; FPInnovations","funders":"Natural Resources Canada; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; Ministère des Ressources Naturelles et de la Faune; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université du Québec à Rimouski","keywords":"Canopy; Environmental science; Taiga; Biomass (ecology); Tree canopy; Black spruce; Lidar; Natural (archaeology); Forest structure; Forest ecology; Physical geography; Ecosystem; Atmospheric sciences; Ecology; Forestry; Geography; Remote sensing; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006020111,0.0001187846,0.0001164111,0.00003277843,0.0001836647,0.00001859084,0.0001442216,0.0000507252,0.00004420554],"category_scores_gemma":[0.00004259135,0.00009951109,0.00002310117,0.0001815905,0.0007188593,0.0001266662,0.000117211,0.0001041468,0.00003760552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004732491,"about_ca_system_score_gemma":0.00001512753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00406973,"about_ca_topic_score_gemma":0.004390059,"domain_scores_codex":[0.999191,0.00002219055,0.0001648141,0.0002464585,0.0001782893,0.0001972819],"domain_scores_gemma":[0.9995323,0.00002312378,0.00009623067,0.0002345904,0.00002052817,0.00009322152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001973716,0.000005526611,0.9382117,0.000006892551,0.000007603857,0.000002297661,0.0004994707,0.004495723,0.05576623,0.00005932215,0.00004183779,0.0008836917],"study_design_scores_gemma":[0.0001650101,0.0001064307,0.9315934,0.00004247817,0.000009655896,0.00002230216,0.00001743224,0.03375025,0.03369174,0.0003142143,0.0001654121,0.0001217252],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995342,0.000008827054,0.00005025404,0.00006290859,0.00004932888,0.0001489399,0.000009901758,0.000023693,0.0001119474],"genre_scores_gemma":[0.997971,0.000001836803,0.001801897,0.0001059405,0.00005187075,5.696639e-7,0.000007692618,0.00001373739,0.00004545574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02925453,"threshold_uncertainty_score":0.6152241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227897402561189,"score_gpt":0.2697846554467366,"score_spread":0.2575056814211247,"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."}}