{"id":"W4411656745","doi":"10.51847/zqdayhk9v1","title":"10.51847/ZQdayHk9v1","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Currency; Business; Monetary economics; Economics; Econometrics; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006957869,0.0008914248,0.0007814581,0.001690914,0.001756279,0.003881266,0.001547728,0.002502829,0.9508612],"category_scores_gemma":[0.001221164,0.0004041471,0.0004947598,0.002313899,0.0008084359,0.002069958,0.003056233,0.001094935,0.9534252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477306,"about_ca_system_score_gemma":0.0007022701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00599369,"about_ca_topic_score_gemma":0.004589017,"domain_scores_codex":[0.9995756,0.00005328883,0.00003182844,0.0001057308,0.0001451446,0.00008846621],"domain_scores_gemma":[0.9993105,0.00009941554,0.00003935103,0.0001631366,0.0002427719,0.0001448663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002319052,0.0001163343,0.0007999624,0.0003937797,0.00001672395,0.0001750211,0.000126033,0.0003384902,0.00193765,0.02012536,0.4262129,0.5495258],"study_design_scores_gemma":[0.00001854152,0.00001529987,0.0004693586,0.00005908841,0.000002876085,0.00008379588,0.00007865913,0.000231357,0.0003110712,0.00152044,0.9972004,0.000009141399],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001155848,0.0004766208,0.001946753,0.00113586,0.0005313611,0.00009569159,0.002219043,0.002129359,0.9903094],"genre_scores_gemma":[0.00500109,0.0002318906,0.001367758,0.0003486279,0.00005166609,0.00004693957,0.001499584,0.0004369018,0.9910156],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.04913878,"threshold_uncertainty_score":0.07009041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00575304976731749,"score_gpt":0.1779685266787294,"score_spread":0.172215476911412,"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."}}