{"id":"W3205594476","doi":"10.3390/jrfm14100499","title":"The Effect of Risk, R&amp;D Intensity, Liquidity, and Inventory on Firm Performance during COVID-19: Evidence from US Manufacturing Industry","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Cash flow; Business; Market liquidity; Cash; Ordinary least squares; Asset (computer security); Test (biology); Logistic regression; Econometrics; Actuarial science; Economics; Finance; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001898575,0.0001848682,0.0004969975,0.0002217773,0.0003854543,0.00009001092,0.0002287132,0.0001470568,0.00002472984],"category_scores_gemma":[0.002989178,0.0001551591,0.0001206797,0.0001407574,0.000107909,0.0002462795,0.0003653542,0.0007833104,0.000007636998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002204332,"about_ca_system_score_gemma":0.00004713888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005255106,"about_ca_topic_score_gemma":0.0001243631,"domain_scores_codex":[0.9985537,0.00009210743,0.0007003912,0.0002764856,0.0001270082,0.0002503065],"domain_scores_gemma":[0.9977103,0.0007393454,0.001027079,0.000294211,0.00003800691,0.0001910248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007319772,0.00002947615,0.9824018,0.0002647884,0.00007912169,0.00006922353,0.0008003016,0.001344661,0.00001080077,0.0001142143,0.0004097766,0.01374388],"study_design_scores_gemma":[0.001324723,0.0002820096,0.975572,0.0003812663,0.0000799022,0.00002300047,0.00007367568,0.0001320065,0.001333497,0.002289841,0.0183311,0.0001770067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919614,0.00615961,0.0007418211,0.0002872812,0.000596525,0.0001389304,0.00003864337,0.000006808486,0.00006901911],"genre_scores_gemma":[0.9683986,0.03098217,0.00008929048,0.0002201672,0.0002218879,0.00000310359,9.686031e-7,0.0000118777,0.00007194517],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02482256,"threshold_uncertainty_score":0.6327203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0327656661734624,"score_gpt":0.2553367080916154,"score_spread":0.222571041918153,"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."}}