{"id":"W3027130718","doi":"10.3758/s13428-020-01403-6","title":"Assessing evidence for replication: A likelihood-based approach","year":2020,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Replication (statistics); Computer science; Contrast (vision); Statistics; Econometrics; Artificial intelligence; 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":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5325486,0.0002615428,0.002304754,0.0004724624,0.0005063638,0.003601109,0.003784219,0.0001356844,0.002454491],"category_scores_gemma":[0.4289657,0.0001427082,0.001656416,0.004518378,0.0001779926,0.0006741504,0.0003144276,0.0005011287,0.0005849621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008120863,"about_ca_system_score_gemma":0.0005135962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001176003,"about_ca_topic_score_gemma":9.614033e-7,"domain_scores_codex":[0.8943338,0.08260906,0.007594947,0.003261099,0.01135137,0.0008497468],"domain_scores_gemma":[0.9143738,0.0635993,0.002797913,0.01072219,0.007515355,0.000991408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003582859,0.0001899617,0.0131707,0.000222803,0.00004156998,0.000005076894,0.0005197228,0.00004712147,0.02028607,0.0008999442,0.04642683,0.9181544],"study_design_scores_gemma":[0.0009583072,0.0006092362,0.0424141,0.0002251203,0.0006575446,0.00001771069,0.004918338,0.3686452,0.01353517,0.006813861,0.5603094,0.0008960079],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01166624,0.001135562,0.9761319,0.005550095,0.00007891258,0.003289295,0.000009250978,0.00001975592,0.002118948],"genre_scores_gemma":[0.1079093,0.000006574377,0.8878068,0.0004111922,0.0002094965,0.002379458,0.00001031558,0.0000267939,0.001240054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9172584,"threshold_uncertainty_score":0.9984574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9916934429120137,"score_gpt":0.8138699872158525,"score_spread":0.1778234556961612,"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."}}