{"id":"W2913249152","doi":"10.1038/s41559-019-0825-2","title":"Author Correction: A statistical estimator for determining the limits of contemporary and historic phenology","year":2019,"lang":"en","type":"erratum","venue":"Nature Ecology & Evolution","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Université du Québec à Montréal","funders":"","keywords":"Phenology; Estimator; Statistics; Mathematics; Coding (social sciences); Demography; Geography; Econometrics; Biology; Ecology; Sociology","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003148813,0.0002860105,0.0005022749,0.00006454489,0.0002396411,0.00001203821,0.0002474042,0.002385912,0.00006707048],"category_scores_gemma":[0.001211428,0.0002001375,0.00008214825,0.0001648091,0.0005276875,0.00008172664,0.0001261346,0.001754622,0.00004051843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008670457,"about_ca_system_score_gemma":0.0001580447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000436677,"about_ca_topic_score_gemma":0.0003586336,"domain_scores_codex":[0.9983226,0.0001943771,0.0003592601,0.0005746704,0.0002351011,0.0003139921],"domain_scores_gemma":[0.9983092,0.0007657129,0.0004947342,0.0002919673,0.00007733164,0.00006105354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00005289116,0.00004090625,0.009233097,0.00004625555,0.00003234824,0.000002319374,0.00008768403,0.00004445618,0.00009549344,0.0002014483,0.9889186,0.001244505],"study_design_scores_gemma":[0.0004403692,0.0005179589,0.5350071,0.00004925086,0.0001368595,0.0000849641,0.00004707507,0.01025657,0.000008043981,0.0005405034,0.4526496,0.0002617165],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.07425039,0.01602672,0.01887813,0.01338273,0.7572566,0.01143072,0.0007903535,0.0005681925,0.1074162],"genre_scores_gemma":[0.8400314,0.00003431704,0.00677612,0.0003869416,0.001399063,0.00003626206,0.000646659,0.00006889438,0.1506204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.765781,"threshold_uncertainty_score":0.9989092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264434853421657,"score_gpt":0.2604946647582329,"score_spread":0.2478503162240163,"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."}}