{"id":"W4291016329","doi":"10.3389/fevo.2022.972179","title":"Using paleoecological data to inform decision making: A deep-time perspective","year":2022,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Geological Survey; University of British Columbia","keywords":"Paleoclimatology; Anthropocene; Paleoecology; Context (archaeology); Climate change; Ecosystem; Biodiversity; Range (aeronautics); Deep time; Global warming; Effects of global warming on oceans; Geologic record; Deep sea; Climate model; Paleontology; Ecology; Geology; Oceanography; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01063178,0.001427935,0.000919212,0.007095101,0.001053322,0.01358706,0.002188567,0.00317162,0.003803193],"category_scores_gemma":[0.02449355,0.0008679508,0.0006206849,0.008688516,0.006231421,0.01630913,0.004213183,0.004113409,0.0004664106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003992921,"about_ca_system_score_gemma":0.003311439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01900127,"about_ca_topic_score_gemma":0.02724742,"domain_scores_codex":[0.9971015,0.001748037,0.0002435811,0.0002972426,0.0004011522,0.0002085513],"domain_scores_gemma":[0.9834374,0.01126253,0.00156563,0.00146073,0.001455817,0.0008178274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001532781,0.0002231479,0.08647079,0.001249686,0.0007897627,0.0008032056,0.003405107,0.06749035,0.001111665,0.3730111,0.01806039,0.4472315],"study_design_scores_gemma":[0.00001659849,0.00004224065,0.01829451,0.001897304,0.0001141514,0.0001877429,0.005393045,0.03458521,0.0009723683,0.8128453,0.1255174,0.0001339437],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1325974,0.1187693,0.282692,0.3144317,0.002472267,0.0002590786,0.01016109,0.0005686831,0.1380484],"genre_scores_gemma":[0.8407822,0.0614663,0.08635897,0.005158036,0.001258685,0.0001200515,0.001940968,0.0001696406,0.002745173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01900127,"threshold_uncertainty_score":0.05622685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133864383563109,"score_gpt":0.3002130128952424,"score_spread":0.2588743690596114,"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."}}