{"id":"W3008428869","doi":"10.1101/2020.02.20.958553","title":"Tree diversity effects on forest productivity increase through time because of spatial partitioning","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Japan Society for the Promotion of Science","keywords":"Afforestation; Monoculture; Biomass (ecology); Carbon sequestration; Canopy; Species diversity; Ecology; Biology; Productivity; Agroforestry; Environmental science","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":[0.0005168922,0.0002636251,0.0003097706,0.0003383739,0.0002165775,0.0004277483,0.0002139916,0.0001805611,0.001414806],"category_scores_gemma":[0.0004439566,0.0001496485,0.0002782303,0.0002120592,0.0004092486,0.0002628657,0.0004765542,0.0004212347,0.0001192572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004179444,"about_ca_system_score_gemma":0.0001967067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001954508,"about_ca_topic_score_gemma":0.003572839,"domain_scores_codex":[0.9997689,0.00005558314,0.00001647538,0.00007437498,0.0000426832,0.00004206188],"domain_scores_gemma":[0.9988427,0.0003581196,0.0002929231,0.0001364355,0.00009112962,0.0002787452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004212131,0.001013501,0.2376012,0.0001286999,0.0003265398,0.0001963567,0.0002117105,0.001596758,0.7364642,0.0003762798,0.0001974131,0.01767524],"study_design_scores_gemma":[0.00002813702,0.0008423341,0.9684881,0.000005311096,0.00006408949,0.00006296994,0.0001109878,0.00145811,0.02835804,0.0002200147,0.0003498873,0.00001201428],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993197,0.00009468471,0.0001958757,0.00001291445,0.000001903637,0.000002840458,0.00006592185,0.000005273781,0.0003009693],"genre_scores_gemma":[0.9993104,0.00004423241,0.0002542207,0.0000237491,0.000003266752,0.000008576944,0.00008158504,0.0000045283,0.0002694301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001954508,"threshold_uncertainty_score":0.004733026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294852907694662,"score_gpt":0.2029581464952401,"score_spread":0.1900096174182935,"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."}}