{"id":"W2202588911","doi":"","title":"Results from Canadian User Needs Survey","year":2010,"lang":"en","type":"article","venue":"University of New Hampshire Scholars Repository (University of New Hampshire at Manchester)","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Business","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001179025,0.0003841511,0.0006530244,0.0004003106,0.002068229,0.0001143627,0.001868653,0.0006680004,0.00102417],"category_scores_gemma":[0.0003588773,0.0005534102,0.00041671,0.0008483718,0.001234705,0.001481132,0.0004611582,0.0007067085,0.000158552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009572988,"about_ca_system_score_gemma":0.002550953,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.846624,"about_ca_topic_score_gemma":0.9182263,"domain_scores_codex":[0.9963283,0.0005748973,0.0004144934,0.0008667949,0.001027925,0.0007875986],"domain_scores_gemma":[0.9955535,0.0004074956,0.0006218147,0.001251112,0.0004388484,0.001727252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001662584,0.0003370379,0.7538171,0.00005760008,0.0004243222,0.000233647,0.1417601,0.00002855143,0.008486875,0.0007563237,0.08833078,0.00410507],"study_design_scores_gemma":[0.002445567,0.0001119991,0.621366,0.0001009171,0.0001427072,0.000004197007,0.03398756,0.0000125427,0.0002960576,0.000213433,0.3407509,0.000568174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621205,0.00006484623,0.00006163368,0.002375841,0.0009410567,0.0004897359,0.0002632933,0.0001142943,0.03356878],"genre_scores_gemma":[0.9146625,0.0001181583,0.001547057,0.00009308356,0.0002124478,4.265217e-8,0.0001775654,0.00002711843,0.08316199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2524201,"threshold_uncertainty_score":0.999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.094961157540299,"score_gpt":0.2898542597100738,"score_spread":0.1948931021697748,"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."}}