{"id":"W2167762607","doi":"10.18438/b8330q","title":"Using Analytic Tools with California School Library Survey Data","year":2015,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; School library; Decision tree; Logistic regression; Best practice; Data collection; Data science; Information retrieval; Library science; Statistics; Data mining; Mathematics; Machine learning","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"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002574719,0.0001640205,0.0001705614,0.0002735601,0.0005394743,0.003858539,0.0009078723,0.00009627066,0.0008842823],"category_scores_gemma":[0.006987253,0.0001331668,0.0000203376,0.001623827,0.0002511725,0.8992569,0.0003095927,0.0002390161,0.0003346969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002019484,"about_ca_system_score_gemma":0.00339279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001905778,"about_ca_topic_score_gemma":3.932854e-7,"domain_scores_codex":[0.9972908,0.000809945,0.0005643988,0.0002257319,0.0007839199,0.0003252203],"domain_scores_gemma":[0.9962014,0.001906193,0.000515738,0.0006126201,0.000133001,0.0006310641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0040528,0.0002224511,0.2083292,0.0002988348,0.0001210226,0.00003438308,0.009535016,0.004587993,0.000005936173,0.5126084,0.2282007,0.03200324],"study_design_scores_gemma":[0.0003462002,0.00009306755,0.01136084,0.0001617332,0.00002235531,0.00001036899,0.004812693,0.0239253,0.00003374147,0.00007773154,0.9589082,0.0002477863],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.05565052,0.002722925,0.03332129,0.575901,0.001580236,0.003700834,0.002384478,0.00218314,0.3225556],"genre_scores_gemma":[0.3071044,0.00248261,0.09754942,0.5851777,0.0008006693,0.00002962368,0.004436647,0.00004629375,0.002372585],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.8953983,"threshold_uncertainty_score":0.9971756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1568561177579565,"score_gpt":0.3573454613546682,"score_spread":0.2004893435967117,"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."}}