{"id":"W4388069349","doi":"10.3390/plants12213735","title":"Predicting the Potential Geographical Distribution of Rhodiola L. in China under Climate Change Scenarios","year":2023,"lang":"en","type":"article","venue":"Plants","topic":"Medicinal Plants and Bioactive Compounds","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Rhodiola; China; Geography; Precipitation; Habitat; Distribution (mathematics); Climate change; Quarter (Canadian coin); Physical geography; Environmental science; Ecology; Biology; Meteorology; Archaeology; Salidroside","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002838528,0.0004460283,0.0002119116,0.0007808715,0.0002264519,0.000510308,0.000611664,0.0003333573,0.000772843],"category_scores_gemma":[0.0004238011,0.0001699322,0.0006019429,0.000761036,0.0002308908,0.0004029924,0.0003441994,0.0001738429,0.0001472827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276507,"about_ca_system_score_gemma":0.0005586303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1246673,"about_ca_topic_score_gemma":0.1029286,"domain_scores_codex":[0.9999108,0.00001563674,0.000006022732,0.00003144426,0.00001100649,0.00002505789],"domain_scores_gemma":[0.9998264,0.00003668038,0.00003669767,0.00001577017,0.00004555771,0.00003893076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001905564,0.00006937974,0.46639,0.0001021846,0.0001373594,0.0006690488,0.0001621385,0.5135745,0.004515707,0.0005471084,0.001267343,0.0123746],"study_design_scores_gemma":[0.00003922924,0.00004752726,0.2880461,0.00001543214,0.00007038398,0.0001103338,0.0003823041,0.7087219,0.0006730256,0.0003665046,0.001500277,0.00002690687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997237,0.0001641904,0.0006108842,0.000114946,0.000004372286,0.000008938528,0.001131756,0.0000566638,0.0006712223],"genre_scores_gemma":[0.9981368,0.0001150652,0.0004558188,0.00001149312,0.000002719171,0.000008409124,0.001074241,0.000005594051,0.0001899626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1246673,"threshold_uncertainty_score":0.2478834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01730326645651906,"score_gpt":0.257405104091983,"score_spread":0.2401018376354639,"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."}}