{"id":"W2901245881","doi":"10.1016/j.palaeo.2018.11.025","title":"Identifying bias in cold season temperature reconstructions by beetle mutual climatic range methods in the Pliocene Canadian High Arctic","year":2018,"lang":"en","type":"article","venue":"Palaeogeography Palaeoclimatology Palaeoecology","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Division of Polar Programs; National Science Foundation","keywords":"Subfossil; Arctic; Vegetation (pathology); Range (aeronautics); Physical geography; Environmental science; Climatology; Paleoclimatology; Ecology; Proxy (statistics); Permafrost; Climate change; Geology; Geography; Holocene; Paleontology; Biology; Statistics","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":[0.002652538,0.000238004,0.0002237295,0.00124636,0.001193877,0.001153878,0.0005945273,0.0002525019,0.0008150789],"category_scores_gemma":[0.005586751,0.0002330716,0.0002671859,0.00146001,0.0003630676,0.0004852714,0.0005674647,0.0002560325,0.000140928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003186505,"about_ca_system_score_gemma":0.002523993,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8147855,"about_ca_topic_score_gemma":0.9354863,"domain_scores_codex":[0.9993777,0.0002183487,0.00003614228,0.0001626166,0.00008822168,0.0001169068],"domain_scores_gemma":[0.9976709,0.0008480116,0.0003502386,0.0001762332,0.0008268627,0.0001276773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000146605,0.000009906547,0.9765924,0.00001919943,0.0002172843,0.00001913869,0.0007386514,0.003441949,0.001837247,0.0001874341,0.0003880681,0.01640222],"study_design_scores_gemma":[0.00001070592,0.000008685859,0.9836331,0.00002869198,0.00008204605,0.00004144645,0.000827138,0.01313664,0.0009247169,0.00008747502,0.001205299,0.00001407949],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974701,0.0003408251,0.00104439,0.0000397463,0.000007905863,0.000002615145,0.0003431983,0.00002635122,0.0007250301],"genre_scores_gemma":[0.9979431,0.00008423244,0.001187015,0.00001415577,0.000004012515,0.000002895284,0.000481515,0.00002504092,0.0002580811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1852145,"threshold_uncertainty_score":0.3726104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02960397600519429,"score_gpt":0.3003757554701429,"score_spread":0.2707717794649486,"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."}}