{"id":"W6917565559","doi":"10.57745/rklsdp","title":"GlycemicExcursions_+2h of recovery.tab","year":2023,"lang":"en","type":"dataset","venue":"Recherche Data Gouv France","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Identification (biology); Product (mathematics); Process (computing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009068266,0.003980014,0.00204601,0.002987211,0.0009013978,0.002930004,0.003244523,0.003834767,0.132999],"category_scores_gemma":[0.005042598,0.000945953,0.002185394,0.004482267,0.0005205381,0.001606379,0.001738257,0.001919502,0.1628316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002127719,"about_ca_system_score_gemma":0.002519518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0435982,"about_ca_topic_score_gemma":0.06085654,"domain_scores_codex":[0.9990211,0.000150207,0.00007774871,0.0003526835,0.0001897863,0.000208489],"domain_scores_gemma":[0.9981748,0.0005288527,0.0001361119,0.0005305969,0.000433183,0.000196543],"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":[0.00009695233,0.00002058947,0.0003090211,0.0003615377,0.0000239369,0.00001099103,0.000008380812,0.0002488469,0.00007546919,0.0002059278,0.9969919,0.001646472],"study_design_scores_gemma":[0.0007717444,0.00007128295,0.004017053,0.0003990031,0.00005376421,0.00009512677,0.00008123056,0.001267301,0.0007525624,0.001765494,0.9906594,0.0000660236],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001433485,0.0001135789,0.00005282415,0.00008591313,0.00004474342,0.000009211689,0.9978597,0.0009490894,0.0007416763],"genre_scores_gemma":[0.0005422056,0.0000838153,0.0002527566,0.00009092739,0.00001689611,0.00003502068,0.9978787,0.0002025752,0.0008971909],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.867001,"threshold_uncertainty_score":0.444926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4085897146495174,"score_gpt":0.4470449539022857,"score_spread":0.03845523925276828,"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."}}