{"id":"W2531324058","doi":"10.1016/j.quascirev.2016.09.024","title":"Development of a new pan-European testate amoeba transfer function for reconstructing peatland palaeohydrology","year":2016,"lang":"en","type":"article","venue":"Quaternary Science Reviews","topic":"Geology and Paleoclimatology Research","field":"Earth and Planetary Sciences","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Environment Research Council; Office Fédéral de l'Education et de la Science; Russian Science Foundation; Bundesministerium für Bildung und Forschung; Russian Foundation for Basic Research; Leverhulme Trust","keywords":"Testate amoebae; Peat; Range (aeronautics); Taxon; Statistical model; Computer science; Statistical hypothesis testing; Physical geography; Ecology; Statistics; Geography; Machine learning; Biology; 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":[0.003184757,0.0001383168,0.0003124972,0.0001623074,0.000290025,0.00001636043,0.0004328718,0.00004676588,0.0005302419],"category_scores_gemma":[0.0001896349,0.00007870797,0.00007233902,0.0002810763,0.0005946764,0.0003042038,0.00001691252,0.00008082935,0.0004593636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002496603,"about_ca_system_score_gemma":0.0002397924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004376449,"about_ca_topic_score_gemma":0.0006118315,"domain_scores_codex":[0.9981499,0.0002219614,0.0005377377,0.000416107,0.0001683388,0.0005059761],"domain_scores_gemma":[0.9992297,0.0002263594,0.0001160879,0.0002111956,0.00005376515,0.0001628925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004122553,0.000004077252,0.6603702,0.00003241302,0.000004955487,0.000002482549,0.0001918304,0.000001361715,0.001466387,0.00002221769,0.00002495842,0.3378378],"study_design_scores_gemma":[0.001297323,0.000701234,0.883781,0.0003449866,0.00003903157,0.0002747831,0.00009546529,0.000606489,0.002349264,0.001604016,0.1084803,0.0004261297],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845372,0.001324887,0.01109612,0.0003052022,0.0003539932,0.0004372377,0.000007226762,0.00001995306,0.001918154],"genre_scores_gemma":[0.990725,0.0008875267,0.007736295,0.00007895267,0.00005677861,0.000004611565,0.0000129929,0.000003044024,0.000494805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3374117,"threshold_uncertainty_score":0.5904343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06933930410172642,"score_gpt":0.2843976394647498,"score_spread":0.2150583353630234,"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."}}