{"id":"W7115095729","doi":"","title":"Replication materials for \"The Early Drivers of Success: Compensation, Accentuation, or Selection Effect?\"","year":2025,"lang":"","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Replication (statistics); Selection (genetic algorithm); Data file; Data collection","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":["metaresearch","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0159427,0.002043777,0.001833919,0.003105419,0.002821207,0.002226566,0.002929548,0.002556726,0.6131369],"category_scores_gemma":[0.1650823,0.001502685,0.003058526,0.004470317,0.001033342,0.002181703,0.002229524,0.004135924,0.2278944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094094,"about_ca_system_score_gemma":0.005002049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008165183,"about_ca_topic_score_gemma":0.01021991,"domain_scores_codex":[0.9897176,0.004638703,0.001834549,0.001376344,0.001589462,0.0008433877],"domain_scores_gemma":[0.8850631,0.04418399,0.006131055,0.04395826,0.01826358,0.002400088],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005482267,0.0001671193,0.001960545,0.0005629025,0.0001157924,0.00008866031,0.0003241757,0.0002616694,0.0003621995,0.003978777,0.9752357,0.01639429],"study_design_scores_gemma":[0.006029367,0.0007262879,0.0457794,0.00164247,0.000564929,0.000380039,0.001658682,0.001899969,0.003790403,0.0343062,0.9028414,0.0003807852],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00605466,0.000215503,0.02833584,0.003541551,0.006035595,0.01020261,0.9027822,0.00851185,0.03432026],"genre_scores_gemma":[0.06971341,0.0004471853,0.07036578,0.00501364,0.001975538,0.1057713,0.5542026,0.01148585,0.1810247],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9970704,"threshold_uncertainty_score":0.5518132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533479735773226,"score_gpt":0.2896389009618733,"score_spread":0.274304103604141,"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."}}