{"id":"W2765148896","doi":"10.1038/s41559-017-0350-0","title":"A statistical estimator for determining the limits of contemporary and historic phenology","year":2017,"lang":"en","type":"article","venue":"Nature Ecology & Evolution","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":117,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"National Science Foundation","keywords":"Phenology; Context (archaeology); Citizen science; Climate change; Event (particle physics); Yesterday; Estimator; Geography; Herbarium; Variation (astronomy); Statistics; Ecology; Data science; Computer science; Biology; Archaeology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001669128,0.00006899268,0.0001226991,0.00001287562,0.000420615,0.00001017657,0.0001703311,0.0002197953,0.000687424],"category_scores_gemma":[0.00068965,0.00005251297,0.00002313811,0.00002121119,0.0005647646,0.00008610271,0.00008890351,0.0001512854,0.00002432334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002977149,"about_ca_system_score_gemma":0.00001691066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002933176,"about_ca_topic_score_gemma":0.00034279,"domain_scores_codex":[0.9994661,0.00003085136,0.0001181963,0.0001669711,0.00006949125,0.0001484294],"domain_scores_gemma":[0.9994237,0.0001808874,0.0001604062,0.0001791173,0.00002120411,0.0000346469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000749156,0.00006818973,0.9292646,0.00001257378,0.00001136662,0.000001349495,0.00007265027,0.000001144537,0.0008682645,0.02782208,0.04052898,0.001273902],"study_design_scores_gemma":[0.0004813518,0.000135137,0.9856109,0.000001856114,0.000015283,0.000004898478,0.00007836778,0.0003408557,0.00005039729,0.001041874,0.01218577,0.00005329705],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907147,0.0002508991,0.0007348791,0.001598042,0.0006089229,0.0002819301,0.0001097243,0.000015689,0.005685268],"genre_scores_gemma":[0.9993629,0.000007589509,0.0003132755,0.0001203869,0.00002793311,0.00003221142,0.00002712657,0.000004566346,0.0001039801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05634633,"threshold_uncertainty_score":0.7526811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03243403379140814,"score_gpt":0.3003375072075968,"score_spread":0.2679034734161887,"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."}}