{"id":"W6948329304","doi":"10.48660/22100148","title":"Session 2 - Viktoriia Voloshyna","year":2022,"lang":"en","type":"other","venue":"PIRSA","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Session (web analytics); Training (meteorology); Attendance; Intervention (counseling)","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":[0.0001478751,0.0002990173,0.0003171229,0.0003505644,0.00007808513,0.00002201339,0.0004170201,0.0002267721,0.3922711],"category_scores_gemma":[0.0000445699,0.0002876892,0.0001096049,0.0003136785,0.00004158825,0.00002595015,0.0003290893,0.0004288382,0.06970944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003263243,"about_ca_system_score_gemma":0.00008510177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004115319,"about_ca_topic_score_gemma":0.0001542286,"domain_scores_codex":[0.9984401,0.0001165396,0.0001451855,0.0004515789,0.0005276843,0.0003189172],"domain_scores_gemma":[0.9989181,0.00002304585,0.0002254691,0.0007249037,0.00001183763,0.00009662588],"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":[0.000008180044,0.00006045412,0.000385634,0.00003724026,0.00005522815,0.00004167021,0.0000556778,7.62373e-7,0.0002949681,0.0002213773,0.9979537,0.0008851059],"study_design_scores_gemma":[0.0002435853,0.00002660366,0.0001243769,0.00005830282,0.00005108845,0.00000515957,0.00002423111,0.000008102223,0.0000116595,0.00007137452,0.9990389,0.0003366618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00003924382,0.001541828,0.000006025417,0.00004012735,0.001588152,0.000278885,0.0007151832,0.001337659,0.9944529],"genre_scores_gemma":[0.0004300498,0.00003177453,0.0002221844,0.0001066502,0.001234774,0.00007962724,0.0006034944,0.002971024,0.9943204],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3225617,"threshold_uncertainty_score":0.9999575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01541590533769758,"score_gpt":0.2688390262280338,"score_spread":0.2534231208903362,"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."}}