{"id":"W2184429365","doi":"10.29173/iasl7765","title":"Researching Data Sets to Develop State Library Standards","year":2021,"lang":"en","type":"article","venue":"IASL Annual Conference Proceedings","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Baseline (sea); Set (abstract data type); Data set; Descriptive statistics; Computer science; School library; State (computer science); Data science; Information retrieval; Statistics; Library science; Mathematics; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001658652,0.0001361705,0.0001670692,0.0001573197,0.0007391275,0.002430051,0.001524474,0.00006405669,0.001351379],"category_scores_gemma":[0.002832687,0.0001283407,0.00002028424,0.002056542,0.0001968994,0.04024346,0.001262202,0.0002495298,0.0002165981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004614532,"about_ca_system_score_gemma":0.00502984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001149401,"about_ca_topic_score_gemma":0.00002237992,"domain_scores_codex":[0.9972134,0.00005565668,0.0003475703,0.0004665491,0.001298634,0.0006182542],"domain_scores_gemma":[0.9969491,0.00008013374,0.00009152174,0.0002404436,0.002150009,0.0004888294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003675141,0.00002717262,0.007669833,0.00004164806,0.00001102828,0.00001443873,0.6628214,8.590308e-7,0.00005252555,0.03041439,0.2403625,0.05854746],"study_design_scores_gemma":[0.0001090159,0.0000370902,0.003369985,0.00009075399,0.000001969009,0.000003284515,0.1291043,0.0001698656,0.000693814,0.004503799,0.8616929,0.0002232351],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7779513,0.00008039232,0.0006280878,0.05410242,0.0003686654,0.000410325,0.001599228,0.00040611,0.1644535],"genre_scores_gemma":[0.9590203,0.0002183367,0.009684231,0.009780153,0.0002470922,0.00001652807,0.0002124005,0.00001859136,0.02080236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6213305,"threshold_uncertainty_score":0.9995615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07140043707918858,"score_gpt":0.3744249094890739,"score_spread":0.3030244724098853,"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."}}