{"id":"W7039340320","doi":"","title":"Looking for work? Understanding the labour market transitions of women and men in Canada","year":2023,"lang":"en","type":"other","venue":"MacSphere (McMaster University)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Unemployment; Affect (linguistics); Business cycle; Transition (genetics); Measure (data warehouse)","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":[0.001623828,0.0003261608,0.0005853337,0.002870858,0.00741381,0.003311061,0.002130972,0.0007847905,0.005083393],"category_scores_gemma":[0.006313898,0.0003512678,0.0007709699,0.008766267,0.001719006,0.001441455,0.00205715,0.002239066,0.0004980386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07830255,"about_ca_system_score_gemma":0.1247729,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9991791,"about_ca_topic_score_gemma":0.9995157,"domain_scores_codex":[0.9982471,0.000115783,0.00005962254,0.0001933363,0.0004252374,0.0009589474],"domain_scores_gemma":[0.996857,0.0004245687,0.0003381366,0.00007850475,0.001144022,0.001157783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002420251,0.000126729,0.8114485,0.0002919904,0.0001491228,0.0002503027,0.03189996,0.00143044,0.0001739837,0.008932089,0.04209926,0.1029557],"study_design_scores_gemma":[0.00002248958,0.00003014037,0.9171408,0.0005148369,0.00006094123,0.00004205512,0.04789343,0.001777739,0.00008859069,0.001880317,0.03048185,0.00006687151],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8635105,0.01877753,0.0008739507,0.04435984,0.0002811205,0.0001762788,0.02946741,0.00004690698,0.04250644],"genre_scores_gemma":[0.9750901,0.008591752,0.0007431603,0.002458621,0.00004307375,0.00007239345,0.005913974,0.0000281456,0.007058687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07830255,"threshold_uncertainty_score":0.5681274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02088688766369205,"score_gpt":0.2174622220293297,"score_spread":0.1965753343656376,"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."}}