{"id":"W3038114514","doi":"10.1136/vr.m2540","title":"Don't waste this crisis","year":2020,"lang":"en","type":"article","venue":"Veterinary Record","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recession; Hospitality; Shadow (psychology); Coronavirus disease 2019 (COVID-19); Government (linguistics); Falling (accident); Quarter (Canadian coin); Business; Pandemic; Political science; Economic policy; Economic growth; Economics; Geography; Medicine; Law; Psychology; Tourism; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001961924,0.0001749026,0.000370884,0.0001206491,0.00007254791,0.00005955222,0.0003143067,0.0001007752,0.004104098],"category_scores_gemma":[0.0001955373,0.0002101414,0.000140895,0.0003022945,0.0000256702,0.0002658929,0.0001719174,0.0001870437,0.00411906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009883937,"about_ca_system_score_gemma":0.00002149839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003601259,"about_ca_topic_score_gemma":0.000002963232,"domain_scores_codex":[0.9986659,0.00001809739,0.0004678986,0.0004735519,0.00003424205,0.0003403142],"domain_scores_gemma":[0.999163,0.00006011157,0.0001971096,0.0003452071,0.00001360553,0.0002209371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00103458,0.0002851633,0.1433903,0.0007148824,0.0003745964,0.0006140862,0.00916121,0.0001256192,0.005230303,0.01108963,0.7793215,0.04865808],"study_design_scores_gemma":[0.000489319,0.0008550276,0.001198398,0.00001094826,0.000005085621,0.0000189177,0.0001231904,0.002147398,0.00009179221,0.001993421,0.9927195,0.0003469648],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9410406,0.0008972568,0.002532761,0.01517418,0.001173787,0.0002401911,0.000149273,0.0002133449,0.03857861],"genre_scores_gemma":[0.9873311,0.0002461052,0.001089741,0.009744142,0.0004055197,0.00001625599,0.000008254594,0.00004040867,0.00111849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.213398,"threshold_uncertainty_score":0.9968063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.11499117858208,"score_gpt":0.2754777375278535,"score_spread":0.1604865589457736,"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."}}