{"id":"W4239061568","doi":"10.6028/jres.124.024","title":"Improving Reproducibility in Research: The Role of Measurement Science","year":2019,"lang":"en","type":"article","venue":"Journal of Research of the National Institute of Standards and Technology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Material Measurement Laboratory; Korea Research Institute of Standards and Science; European Commission; Wellcome Trust","keywords":"Reproducibility; Computer science; Data science; Medical physics; Statistics; Medicine; Mathematics","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"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.8576499,0.003218021,0.01352226,0.01967401,0.008596658,0.03576624,0.01101823,0.01449412,0.003419441],"category_scores_gemma":[0.9286374,0.004426161,0.006321013,0.01888895,0.07944822,0.04794671,0.02743891,0.02559578,0.001184424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0258912,"about_ca_system_score_gemma":0.05561035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009432243,"about_ca_topic_score_gemma":0.00604461,"domain_scores_codex":[0.05570072,0.8277045,0.04288175,0.0221639,0.04942466,0.002124591],"domain_scores_gemma":[0.01436797,0.907368,0.01670822,0.03841965,0.02181031,0.001325909],"domain_codex":"methods","domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006974773,0.0002866618,0.02580157,0.03973377,0.008758096,0.0004120323,0.03710984,0.004823603,0.0006764528,0.4324089,0.03887805,0.4104135],"study_design_scores_gemma":[0.0003897389,0.0006955721,0.008611713,0.03395461,0.001158698,0.0004800379,0.004998576,0.006297661,0.0009031648,0.8648907,0.07702713,0.0005923997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.007243042,0.2643344,0.3160607,0.3752972,0.02001156,0.001797086,0.0004745804,0.0007211561,0.01406038],"genre_scores_gemma":[0.4430965,0.07200186,0.3648261,0.08360049,0.02515367,0.008218964,0.000508421,0.001373006,0.001221031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1423501,"threshold_uncertainty_score":0.1878547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7945497748693895,"score_gpt":0.6115260820717491,"score_spread":0.1830236927976404,"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."}}