{"id":"W2617781829","doi":"10.1111/gcb.13741","title":"Optimal climate for large trees at high elevations drives patterns of biomass in remote forests of Papua New Guinea","year":2017,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Altitude (triangle); Environmental science; Biomass (ecology); Precipitation; Temperate climate; Biosphere; Temperate rainforest; Atmospheric sciences; Carbon cycle; Rainforest; Climate change; Range (aeronautics); Deforestation (computer science); Tropics; Physical geography; Ecology; Ecosystem; Geography; Geology; 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":[0.0002052759,0.00009623061,0.0001316254,0.0003689971,0.0002642424,0.0004193773,0.000206784,0.0001344449,0.000833143],"category_scores_gemma":[0.0004184746,0.0001253622,0.0001258791,0.0002385285,0.0003625066,0.0001599514,0.0002563256,0.0001266466,0.00007445044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002589764,"about_ca_system_score_gemma":0.0001873976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01502605,"about_ca_topic_score_gemma":0.02804657,"domain_scores_codex":[0.9999372,0.00001496012,0.00000344454,0.00002189764,0.000005550495,0.00001689377],"domain_scores_gemma":[0.9997821,0.00007616392,0.00007709979,0.00001809314,0.00001841979,0.00002807574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005968419,0.00004364832,0.9865237,0.0000226661,0.000033807,0.0001103893,0.0008084225,0.0006466479,0.006807188,0.0001615721,0.0000930194,0.004689337],"study_design_scores_gemma":[0.000001392069,0.000007592928,0.999238,0.000003636975,0.000002930896,0.00002537872,0.0001639029,0.0003853498,0.00006721578,0.00004430984,0.00005888451,0.000001349111],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996449,0.00001765201,0.00004513338,0.000008272228,5.781269e-7,0.000001306557,0.0000307289,0.000002091232,0.0002493459],"genre_scores_gemma":[0.9998412,0.0000118641,0.00007069726,0.000005106072,7.17877e-7,0.000003166024,0.00003028456,8.846846e-7,0.00003608085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01502605,"threshold_uncertainty_score":0.02987719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03209071899308631,"score_gpt":0.309589798013521,"score_spread":0.2774990790204347,"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."}}