{"id":"W2047912726","doi":"10.1016/j.foreco.2013.05.046","title":"Modeling tree spatial distributions after partial harvesting in uneven-aged boreal forests using inhomogeneous point processes","year":2013,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Ministère des Forêts, de la Faune et des Parcs","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Taiga; Boreal; Logging; Tree (set theory); Point pattern analysis; Forest management; Biodiversity; Ecology; Clearcutting; Spatial ecology; Environmental science; Geography; Mathematics; Agroforestry; Forestry; Biology","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":[0.0001918332,0.0002064572,0.000180318,0.0001092428,0.0002129477,0.00007244416,0.0001725584,0.00008674007,0.000643514],"category_scores_gemma":[0.00003828683,0.0001971478,0.00003569602,0.0002527606,0.0001760464,0.0003880982,0.0005351739,0.0001178097,0.0002232873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001343191,"about_ca_system_score_gemma":0.000009620885,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01115669,"about_ca_topic_score_gemma":0.1537572,"domain_scores_codex":[0.9985355,0.00005398821,0.0003203784,0.0003961024,0.0001383911,0.0005556845],"domain_scores_gemma":[0.9996009,0.00002772479,0.00006614166,0.0001902443,0.000008921927,0.0001060358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005676808,0.0001489084,0.9184847,0.0001222291,0.00003604878,0.0001124627,0.0002465337,0.07334633,0.000008458328,0.001127144,0.0008659996,0.005444347],"study_design_scores_gemma":[0.0006101914,0.00008392239,0.7699841,0.00003003812,0.0000489584,0.000008037417,0.00002787367,0.2246179,0.000009371144,0.003520676,0.0008157986,0.000243176],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887212,0.0000248749,0.005602467,0.0002946745,0.0001057247,0.0008521264,0.00000811552,0.00004524053,0.004345564],"genre_scores_gemma":[0.9972787,0.0000418218,0.001140694,0.0001811033,0.00006251698,0.0003868471,0.00004292077,0.00001680016,0.0008485491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1512716,"threshold_uncertainty_score":0.9954281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073052126731094,"score_gpt":0.2239176317579629,"score_spread":0.2131871104906519,"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."}}