{"id":"W1500417170","doi":"10.1002/bult.2014.1720400310","title":"ASIS&T annual meeting pre‐conference activities: Full room for the third SIG/MET workshop","year":2014,"lang":"en","type":"article","venue":"Bulletin of the Association for Information Science and Technology","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Informetrics; Scopus; Bibliometrics; Library science; Citation; Scientometrics; Context (archaeology); Session (web analytics); Webometrics; Psychology; Computer science; World Wide Web; MEDLINE; Political science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0406874,0.0001125808,0.0002373489,0.008250629,0.001244891,0.00123195,0.002897473,0.0002036888,0.00002238601],"category_scores_gemma":[0.216145,0.00006227219,0.0001024717,0.03354103,0.0005300724,0.0008718109,0.0007744836,0.0002020609,0.00002608568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001524707,"about_ca_system_score_gemma":0.0002672799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002568302,"about_ca_topic_score_gemma":0.00002020035,"domain_scores_codex":[0.9943494,0.00008120415,0.0007326332,0.000292245,0.004053481,0.000491017],"domain_scores_gemma":[0.9779,0.01151119,0.00147534,0.0006871494,0.00834647,0.00007981368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000108092,0.00007766397,0.02557224,0.00003657185,0.00004794111,1.327194e-8,0.002557016,0.000423592,0.001454614,0.1348198,0.1732218,0.6616806],"study_design_scores_gemma":[0.0004789343,0.0001512044,0.006035709,0.00001698117,0.00001157392,0.000001636378,0.003969518,0.007256904,0.002332658,0.01471134,0.9649132,0.0001203323],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6071571,0.0003780074,0.1692257,0.1842751,0.00267422,0.005257071,0.0005794989,0.0002531707,0.03020016],"genre_scores_gemma":[0.9942154,0.00004655389,0.002810217,0.0003856136,0.00003764191,0.0001176225,0.000002342196,0.000003761795,0.002380798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7916914,"threshold_uncertainty_score":0.9998049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1441817719441936,"score_gpt":0.4341499481024922,"score_spread":0.2899681761582986,"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."}}