{"id":"W7018051249","doi":"","title":"Community profile, Worklink Workforce Investment Area","year":2013,"lang":"en","type":"article","venue":"The South Carolina State Library Digital Collections (South Carolina State Library)","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workforce; Investment (military); Directory; Workforce development; Census; Quarter (Canadian coin)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004724726,0.0004859586,0.0003408442,0.004153957,0.001400425,0.001695057,0.0006867095,0.0004642606,0.4931551],"category_scores_gemma":[0.003104004,0.0003064171,0.0001910493,0.006392635,0.0000898513,0.001542776,0.001144008,0.0007559154,0.4327313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004042579,"about_ca_system_score_gemma":0.00182266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02238276,"about_ca_topic_score_gemma":0.04564388,"domain_scores_codex":[0.9995353,0.00003633247,0.00003901497,0.00005329837,0.0002502127,0.00008587802],"domain_scores_gemma":[0.9972501,0.0002697355,0.0001765081,0.0002091012,0.001503982,0.0005904542],"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":[0.00001739473,0.00004180738,0.001246069,0.00006245836,9.787266e-7,0.00001774434,0.00007834152,0.00001390973,0.00007799659,0.0002474652,0.9738964,0.02429942],"study_design_scores_gemma":[0.00001690643,0.00002034598,0.01159724,0.00007595624,0.000002425771,0.00006141405,0.0003455872,0.00006081508,0.0001041064,0.0002429466,0.9874607,0.00001148821],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.004491854,0.000252083,0.001061053,0.0007548645,0.0003073977,0.0005853618,0.5325259,0.00383726,0.4561843],"genre_scores_gemma":[0.007807335,0.0006385609,0.001778605,0.0006566067,0.000200537,0.0007258159,0.3822831,0.001419389,0.60449],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4931551,"threshold_uncertainty_score":0.7229528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0269620449298264,"score_gpt":0.2306543024472427,"score_spread":0.2036922575174163,"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."}}