{"id":"W4379375122","doi":"10.3386/w31313","title":"Laboratory Safety and Research Productivity","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Chemical Safety and Risk Management","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; University of California, Davis","keywords":"Productivity; Data science; Engineering; Computer science; Economics; Economic growth","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01410661,0.0002316471,0.0004651173,0.002952198,0.001480776,0.004819026,0.00119797,0.001146554,0.01146494],"category_scores_gemma":[0.08839513,0.0002489573,0.0004706023,0.002913853,0.004108583,0.00362257,0.003200669,0.001289747,0.002274514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004974404,"about_ca_system_score_gemma":0.007138513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004190943,"about_ca_topic_score_gemma":0.004459478,"domain_scores_codex":[0.9845862,0.005690996,0.001178437,0.0009711587,0.00579407,0.001779128],"domain_scores_gemma":[0.8013082,0.07475913,0.07545303,0.01913464,0.01578001,0.01356495],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007665033,0.001229239,0.4730097,0.0005329185,0.00040082,0.0005588598,0.003868819,0.007445205,0.005175344,0.1228045,0.03408664,0.3501213],"study_design_scores_gemma":[0.0002020004,0.001420316,0.6768181,0.0007112604,0.0001213722,0.0008423461,0.008499123,0.003518494,0.008128247,0.1325499,0.1670053,0.0001835537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6883301,0.006641648,0.02445501,0.05422276,0.0006711474,0.0002938437,0.002033031,0.0004665351,0.222886],"genre_scores_gemma":[0.9813747,0.00164306,0.002924541,0.002527127,0.0003113277,0.000103686,0.0003616006,0.00004601826,0.01070789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9858934,"threshold_uncertainty_score":0.0746038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3675421174425395,"score_gpt":0.5189377580103703,"score_spread":0.1513956405678308,"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."}}