{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000273771,0.0001303858,0.0001098341,0.0003527062,0.0001820917,0.0003120221,0.0001493866,0.0001388155,0.0003532047],"category_scores_gemma":[0.0006373063,0.00008642957,0.0001361679,0.0002639923,0.0002676116,0.0003054258,0.0003563231,0.0001095199,0.00005187261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487519,"about_ca_system_score_gemma":0.0001250523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002185591,"about_ca_topic_score_gemma":0.007723823,"domain_scores_codex":[0.9997895,0.00005399429,0.0000142702,0.00004592721,0.00005185834,0.00004437486],"domain_scores_gemma":[0.9995548,0.0001660423,0.000136547,0.0000333522,0.00006070091,0.00004855392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001962504,0.00008971772,0.9537279,0.00003852159,0.00004708507,0.0002098718,0.0004526689,0.002244286,0.02210671,0.0001217433,0.00004428204,0.02072105],"study_design_scores_gemma":[0.000002440557,0.00008569335,0.9948695,0.000005182362,0.00001249991,0.0001018307,0.0003855269,0.002697818,0.001611744,0.0000429296,0.0001801436,0.000004659975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999602,0.00002346655,0.0001615999,0.000001825796,5.443652e-7,0.000001171236,0.00001666855,0.000002692287,0.0001899195],"genre_scores_gemma":[0.9997262,0.00001317838,0.0001978835,0.000001437399,7.966406e-7,0.000001105943,0.00002967334,8.380439e-7,0.00002889424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002185591,"threshold_uncertainty_score":0.004345715,"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."}}