{"id":"W4241415242","doi":"10.1111/j.1654-1103.2004.tb02326.x","title":"Advances in spatial, individual‐based modelling of forest dynamics","year":2004,"lang":"en","type":"article","venue":"Journal of Vegetation Science","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts","funders":"","keywords":"Propagule; Canopy; Patch dynamics; Spatial ecology; Forest dynamics; Ecology; Tree (set theory); Vegetation (pathology); Common spatial pattern; Computer science; Environmental science; Ecosystem; Mathematics; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001086413,0.000365145,0.0006622956,0.0004713557,0.0003013518,0.001712858,0.001349929,0.0008187379,0.002392352],"category_scores_gemma":[0.003293989,0.0003757313,0.0006334646,0.001209453,0.0008723867,0.002316675,0.001137911,0.001131134,0.0004048256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009453303,"about_ca_system_score_gemma":0.001053997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01995265,"about_ca_topic_score_gemma":0.013854,"domain_scores_codex":[0.9996051,0.000169465,0.00002220199,0.00007651004,0.00009489121,0.00003190703],"domain_scores_gemma":[0.9984179,0.000911426,0.0002240832,0.0001639551,0.0001906187,0.00009193347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005558836,0.00001094538,0.001359583,0.00001844632,0.00002145864,0.00001148217,0.00002563987,0.9814693,0.0001440964,0.01109834,0.0003035242,0.00553175],"study_design_scores_gemma":[0.00000316419,0.000004011868,0.0004940698,0.000005332723,0.000005713641,0.000008484009,0.000009772227,0.9859234,0.00004939962,0.01230701,0.001184535,0.000005084697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1820433,0.002274281,0.7919565,0.002167009,0.0001863347,0.00004077295,0.001005245,0.0004775088,0.0198491],"genre_scores_gemma":[0.921217,0.001986401,0.07097698,0.0001486469,0.0001411782,0.00008116711,0.0004172335,0.00008563313,0.004945607],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01995265,"threshold_uncertainty_score":0.03967303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02135567892841053,"score_gpt":0.2741776240444914,"score_spread":0.2528219451160809,"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."}}