{"id":"W3166603299","doi":"10.3917/entin.047.0076","title":"Réseaux sociaux et régulation des émotions : le cas de LiveMentor","year":2021,"lang":"fr","type":"article","venue":"Entreprendre & Innover","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Musée de la Civilisation","funders":"","keywords":"Humanities; Political science; Coronavirus disease 2019 (COVID-19); Art; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006701245,0.0002654604,0.0004131184,0.0002173692,0.0002740093,0.0002456431,0.0001922421,0.0002988901,0.008067397],"category_scores_gemma":[0.00110096,0.0004112598,0.0002467914,0.0007661011,0.0001660203,0.0007146581,0.0002238892,0.0003837069,0.001327034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735412,"about_ca_system_score_gemma":0.0007884411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003872579,"about_ca_topic_score_gemma":0.00176622,"domain_scores_codex":[0.9976829,0.0001148408,0.0007255588,0.0006530948,0.00009003194,0.0007336084],"domain_scores_gemma":[0.9985142,0.0002212425,0.0003907577,0.0005620802,0.0001509357,0.0001607807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001355949,0.0006907125,0.4943895,0.0001817545,0.0003257447,0.00007612232,0.01400762,0.002379475,0.0003604559,0.4371423,0.04153717,0.008895512],"study_design_scores_gemma":[0.00131706,0.00004325743,0.5212615,0.0001458527,0.00004863214,0.00004163741,0.0005744633,0.002420751,0.001195316,0.07825422,0.3941917,0.000505581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7800315,0.03183941,0.0687541,0.07005554,0.004716808,0.0004543556,0.001417112,0.0001556564,0.04257552],"genre_scores_gemma":[0.9286363,0.002060632,0.002175877,0.003819675,0.0005104112,0.00001872649,0.0002005751,0.00005976337,0.06251808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3588881,"threshold_uncertainty_score":0.9998339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05731407985656597,"score_gpt":0.304718422869117,"score_spread":0.2474043430125511,"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."}}