{"id":"W7033402926","doi":"","title":"Profiling for customers at risk from long-term unemployment : the Canadian case 
\\n","year":2015,"lang":"en","type":"other","venue":"Warwick Research Archive Portal (University of Warwick)","topic":"Plant Diversity and Evolution","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Profiling (computer programming); Unemployment; Job loss; Data collection","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":[0.001971201,0.0002550239,0.0002547656,0.001373039,0.016046,0.004063523,0.001850074,0.003423968,0.0155012],"category_scores_gemma":[0.00821,0.0003288303,0.0003400623,0.003209481,0.001961837,0.001583706,0.001979873,0.003514485,0.001045653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03326286,"about_ca_system_score_gemma":0.08222273,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9812459,"about_ca_topic_score_gemma":0.9946752,"domain_scores_codex":[0.9969267,0.0002362291,0.0000506177,0.00009828475,0.0006838351,0.002004266],"domain_scores_gemma":[0.9932079,0.0008064644,0.0004482695,0.0001463639,0.001961282,0.003429759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002646254,0.0006394045,0.3727503,0.0001404732,0.00002924894,0.007366741,0.03880841,0.0004022098,0.0004898498,0.02021702,0.4087624,0.1501295],"study_design_scores_gemma":[0.00005593312,0.0001367784,0.4563527,0.000875447,0.00008830603,0.003625504,0.305725,0.002196643,0.0003588708,0.003716057,0.226664,0.0002047627],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5988724,0.003231028,0.0005247165,0.1679857,0.000285671,0.0002824132,0.001987456,0.00006413538,0.2267665],"genre_scores_gemma":[0.9341295,0.003049795,0.0006054812,0.01476379,0.0001023794,0.00006404943,0.0004630222,0.0000584026,0.04676365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03326286,"threshold_uncertainty_score":0.2413401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05856371198998181,"score_gpt":0.2555683822112976,"score_spread":0.1970046702213158,"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."}}