{"id":"W1492547401","doi":"10.18438/b82g7v","title":"Analyzing the MISO Data: Broader Perspectives on Library and Computing Trends","year":2013,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scale (ratio); Point (geometry); Medical education; Survey data collection; Library science; Computer science; Psychology; World Wide Web; Medicine; Mathematics; Geography; Statistics","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":[0.0007326174,0.0001372487,0.0001135084,0.0002613398,0.001218166,0.003775587,0.0006256858,0.00006959365,0.001442192],"category_scores_gemma":[0.000980453,0.00009672238,0.00002711226,0.0009833561,0.0003443354,0.8218619,0.0002716644,0.0002704592,0.0001070439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004879838,"about_ca_system_score_gemma":0.0001965579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002903058,"about_ca_topic_score_gemma":1.324353e-8,"domain_scores_codex":[0.9983556,0.0004362385,0.0004054458,0.0002096045,0.0003384606,0.0002547046],"domain_scores_gemma":[0.996392,0.00263585,0.0003660185,0.0003949892,0.00003481874,0.0001764],"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":[0.00006755735,0.00002908187,0.001995373,0.0000315235,0.00001608951,6.717731e-7,0.0213982,0.00006875979,0.000002912258,0.8271246,0.03270516,0.1165601],"study_design_scores_gemma":[0.0001600686,0.00006874924,0.01953228,0.0001209221,0.0000121307,0.000003875298,0.05615732,0.01506521,0.00003056918,0.0001685172,0.9085104,0.0001699839],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01678758,0.002070005,0.0008968348,0.847842,0.0002525361,0.0004979533,0.00002359631,0.0003365849,0.1312929],"genre_scores_gemma":[0.6507633,0.009340183,0.008856306,0.3271347,0.0006356383,0.00001591312,0.0002073036,0.00001588696,0.003030692],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.8758052,"threshold_uncertainty_score":0.9994707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02512822736145362,"score_gpt":0.3033131668728974,"score_spread":0.2781849395114439,"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."}}