{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.1419293,0.0006341556,0.001025763,0.01466987,0.003552617,0.007281356,0.003371911,0.001136484,0.004862772],"category_scores_gemma":[0.3737968,0.001003482,0.001824008,0.02185735,0.003006974,0.006616301,0.005267112,0.003263166,0.001051997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01165508,"about_ca_system_score_gemma":0.02005037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03363586,"about_ca_topic_score_gemma":0.03304433,"domain_scores_codex":[0.8748326,0.05597011,0.02142983,0.008946942,0.03593179,0.002888692],"domain_scores_gemma":[0.512518,0.2467342,0.02878172,0.05210871,0.1569326,0.002924775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003504193,0.001875276,0.6543821,0.001103083,0.0006842989,0.00008948411,0.01628794,0.008758786,0.001033059,0.1229904,0.03934679,0.1530984],"study_design_scores_gemma":[0.0004190926,0.001688739,0.6549638,0.003130079,0.000573291,0.0001608437,0.04464819,0.03318505,0.009647278,0.04603781,0.2052472,0.0002986215],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5850994,0.0005550376,0.2342846,0.005617526,0.0005421713,0.01716698,0.04767199,0.001517649,0.1075447],"genre_scores_gemma":[0.6735694,0.000306313,0.2083875,0.0010581,0.00009642151,0.03672597,0.07620581,0.0004398414,0.003210603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9927186,"threshold_uncertainty_score":0.7506028,"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."}}