{"id":"W2151458409","doi":"10.1139/x99-188","title":"Spatial simulation of forest succession and timber harvesting using LANDIS","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":290,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Forest Service; U.S. Fish and Wildlife Service","keywords":"Ecological succession; Clearcutting; Environmental science; Forest management; Disturbance (geology); Forest inventory; Thinning; Forest dynamics; Stand development; Seed dispersal; Vegetation (pathology); Biological dispersal; Ecology; Agroforestry; Forestry; Geography; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000709436,0.000048431,0.0001003351,0.0001313704,0.0003237339,0.00002389123,0.0001003909,0.00005009864,0.0009792534],"category_scores_gemma":[0.0002140734,0.00004118532,0.00002215216,0.0001704968,0.0003719602,0.0002023026,0.0000206562,0.0001929452,0.00001294877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125932,"about_ca_system_score_gemma":0.0001237177,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02919932,"about_ca_topic_score_gemma":0.5640109,"domain_scores_codex":[0.9991987,0.000090436,0.0002024108,0.00007867302,0.0002022585,0.0002275158],"domain_scores_gemma":[0.9994125,0.0001837688,0.00007518962,0.00005667906,0.00006267589,0.0002091536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001223738,0.000004526995,0.7581986,0.000005129461,0.000004766131,0.00001789142,0.0002493707,0.2386813,0.00002478088,0.00002224857,0.00003734018,0.00274186],"study_design_scores_gemma":[0.000192243,0.00008760803,0.9003763,0.00004048217,0.000005087589,0.00001977738,0.00004822986,0.0976866,0.000009979178,0.001073058,0.0004220917,0.0000386148],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961028,0.00007270098,0.000368801,0.0001366208,0.00003314202,0.00006205436,0.000002982871,8.706228e-7,0.003219998],"genre_scores_gemma":[0.9991164,0.00001548929,0.0003593808,0.0000108984,0.00003833788,4.797772e-7,0.000001212777,0.000004988388,0.0004528606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5348116,"threshold_uncertainty_score":0.999934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04389674326056052,"score_gpt":0.3316536798943432,"score_spread":0.2877569366337827,"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."}}