{"id":"W4242753874","doi":"10.31234/osf.io/hvfmr","title":"Preregistration of secondary data analysis: A template and tutorial","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Popularity; Computer science; Data science; Selection (genetic algorithm); Psychology; Artificial intelligence; Social psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05971066,0.002695989,0.002077906,0.00583047,0.001620549,0.006345951,0.004494342,0.003721443,0.07895896],"category_scores_gemma":[0.2256314,0.003429788,0.003638565,0.005940196,0.001662763,0.007730025,0.005124536,0.005523845,0.07533453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002342417,"about_ca_system_score_gemma":0.009821009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204276,"about_ca_topic_score_gemma":0.003120329,"domain_scores_codex":[0.9737134,0.0143258,0.005008044,0.001709247,0.004677308,0.0005661637],"domain_scores_gemma":[0.790768,0.1457179,0.01001159,0.02149378,0.02901001,0.00299877],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003301936,0.0002480785,0.0008263304,0.004538304,0.0001175905,0.0009949384,0.005316616,0.002777242,0.004210756,0.04750913,0.5684703,0.3646605],"study_design_scores_gemma":[0.0001024833,0.0001139229,0.0006513171,0.002672615,0.00003898843,0.0009343741,0.0005548412,0.00350458,0.002624241,0.03431944,0.9543587,0.0001244955],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009742118,0.001579139,0.9310399,0.006959218,0.003691956,0.007843371,0.008723697,0.02312043,0.01606806],"genre_scores_gemma":[0.003853652,0.002690392,0.936561,0.003423944,0.001527823,0.01096987,0.006810192,0.0132686,0.02089443],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9402893,"threshold_uncertainty_score":0.315784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07596075832715285,"score_gpt":0.3757982647036783,"score_spread":0.2998375063765254,"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."}}