{"id":"W2982376955","doi":"10.1007/s13595-019-0885-0","title":"Predicting growth and habitat responses of Ginkgo biloba L. to climate change","year":2019,"lang":"en","type":"article","venue":"Annals of Forest Science","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Doctorate Fellowship Foundation of Nanjing Forestry University; Science and Technology Support Program of Jiangsu Province; Government of Jiangsu Province; Nanjing Forestry University","keywords":"Climate change; Habitat; Range (aeronautics); Species distribution; Ecology; Context (archaeology); Environmental science; Latitude; Representative Concentration Pathways; Global warming; Ginkgo biloba; Geography; Biology; Climate model","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":[0.000495471,0.0005681011,0.0002343292,0.0006217018,0.0002086705,0.0004294342,0.0003815954,0.0003069218,0.0008098346],"category_scores_gemma":[0.0006392027,0.0001152287,0.0006185221,0.0004627699,0.0001611151,0.0004322895,0.0002960468,0.0002300597,0.0001977848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009521795,"about_ca_system_score_gemma":0.0004190732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02935975,"about_ca_topic_score_gemma":0.02594096,"domain_scores_codex":[0.9998995,0.00002295542,0.00000498761,0.0000388128,0.00001454758,0.00001912079],"domain_scores_gemma":[0.9997838,0.00007639379,0.00004622201,0.00001772749,0.00004294495,0.00003297465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002240475,0.000124488,0.5463991,0.00009175934,0.0001499181,0.0001353319,0.00009611189,0.4236307,0.01361844,0.0002184251,0.0005366614,0.014775],"study_design_scores_gemma":[0.00001184049,0.00006263545,0.3891514,0.000006103272,0.00002857355,0.00003366992,0.00008599277,0.608718,0.001315971,0.0002020403,0.0003652001,0.0000186267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968991,0.00003005923,0.002175323,0.00002393612,0.000003156091,0.000006192569,0.0005394316,0.00007795879,0.0002448526],"genre_scores_gemma":[0.9982401,0.0000150618,0.001056537,0.000005615011,0.000001273285,0.000008639874,0.0005901608,0.000008289549,0.00007420001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02935975,"threshold_uncertainty_score":0.05837774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06351091216476758,"score_gpt":0.3088101200625746,"score_spread":0.245299207897807,"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."}}