{"id":"W4234956099","doi":"10.1007/s10531-005-5397-6","title":"Impacts of Selective Logging and Agricultural Clearing on Forest Structure, Floristic Composition and Diversity, and Timber Tree Regeneration in the Ituri Forest, Democratic Republic of Congo","year":2005,"lang":"en","type":"article","venue":"Biodiversity and Conservation","topic":"African Botany and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; International Tropical Timber Organization","keywords":"Secondary forest; Logging; Agroforestry; Old-growth forest; Biodiversity; Deforestation (computer science); Geography; Forest farming; Forest restoration; Forest management; Ecology; Forestry; Forest ecology; Biology; Ecosystem","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":[0.0001077768,0.00006366844,0.0001051197,0.00001596988,0.0005014141,0.00001653885,0.00003231017,0.00005098374,0.000001433892],"category_scores_gemma":[0.0000250749,0.00002842869,0.00001030172,0.00008308447,0.0001465091,0.000167089,0.0001039397,0.00004933184,8.280217e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001297273,"about_ca_system_score_gemma":0.000001836836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006403663,"about_ca_topic_score_gemma":0.009514282,"domain_scores_codex":[0.9996037,0.0000546664,0.00008685141,0.0001184381,0.00006320574,0.00007313512],"domain_scores_gemma":[0.9996762,0.000163131,0.00008533216,0.00001590329,0.00003976233,0.00001964149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005889526,0.00001708479,0.9935836,0.00001131565,0.00001259503,4.340881e-7,0.001032115,0.00001196571,0.004172368,0.0003464984,0.0001482035,0.000604902],"study_design_scores_gemma":[0.0002274105,0.0001952201,0.9972749,0.00001211933,0.00002851971,0.000006026625,0.001228412,0.000308271,0.00023906,0.0004160114,0.00001160162,0.00005242649],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978004,0.00008876489,0.000001812473,0.001917862,0.000009843929,0.0001267,0.00002068481,0.000004702148,0.00002929148],"genre_scores_gemma":[0.9996723,0.00008024977,0.00003451775,0.0001710368,0.00001685488,6.210591e-7,0.00002082511,1.397609e-7,0.000003451272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008873916,"threshold_uncertainty_score":0.5309193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02428070297560566,"score_gpt":0.1944916245413414,"score_spread":0.1702109215657357,"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."}}