{"id":"W6995884760","doi":"","title":"Prévision conditionnelle dans un cadre riche en données","year":2022,"lang":"fr","type":"other","venue":"Archipelago (Université du Québec à Montréal)","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Population; Traditional economy","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":[],"consensus_categories":[],"category_scores_codex":[0.03326626,0.001019584,0.002638257,0.005475415,0.00222861,0.01053569,0.0027215,0.002537027,0.01541859],"category_scores_gemma":[0.1062341,0.001437588,0.002462262,0.005363044,0.001672302,0.008582726,0.003587323,0.00316669,0.002135692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006098371,"about_ca_system_score_gemma":0.01035283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09341291,"about_ca_topic_score_gemma":0.07033049,"domain_scores_codex":[0.977452,0.008802383,0.001835989,0.005487964,0.004795121,0.001626516],"domain_scores_gemma":[0.8522955,0.1191154,0.007826851,0.007255604,0.01122455,0.002282128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005034317,0.0007240424,0.2704303,0.003234844,0.002788303,0.001998266,0.005335112,0.2740231,0.008805845,0.08524211,0.02735704,0.3150268],"study_design_scores_gemma":[0.0004526028,0.001419256,0.1604194,0.001605526,0.001171093,0.001385317,0.00704865,0.5921704,0.01156418,0.1267432,0.09526341,0.0007570077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.662448,0.01249591,0.2519683,0.01089753,0.0005485879,0.001271734,0.0298796,0.002474617,0.02801568],"genre_scores_gemma":[0.8381917,0.00215163,0.1289726,0.0007907,0.0001578278,0.0006360558,0.01688534,0.0003158136,0.01189823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09341291,"threshold_uncertainty_score":0.1857384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275209266764274,"score_gpt":0.1563727483399137,"score_spread":0.1436206556722709,"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."}}