{"id":"W6950084680","doi":"10.5281/zenodo.7310985","title":"Demonstrating Librarian Research Impact through Bibliometrics","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Methane Hydrates and Related Phenomena","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bibliometrics; Information system; Citation analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics","metaresearch"],"domain":"evaluation","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch","bibliometrics"],"domain":"evaluation","study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.03171825,0.0007019356,0.001154437,0.03714338,0.002116438,0.01028218,0.00122955,0.002121107,0.02119555],"category_scores_gemma":[0.11,0.0003645572,0.0009965511,0.06113052,0.001515946,0.008403738,0.004592845,0.001602685,0.00655499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00206371,"about_ca_system_score_gemma":0.003002925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00235528,"about_ca_topic_score_gemma":0.003705519,"domain_scores_codex":[0.9840074,0.006623355,0.0009346539,0.001174379,0.006680365,0.0005797601],"domain_scores_gemma":[0.9022336,0.06593043,0.004545747,0.00555901,0.01707596,0.004655151],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004314776,0.0002517442,0.03628049,0.002250123,0.0008990412,0.0001790771,0.001793104,0.001025388,0.00214348,0.02865997,0.6360402,0.290046],"study_design_scores_gemma":[0.0001980371,0.0005326357,0.131317,0.00295291,0.001203439,0.0005736649,0.008302104,0.006525902,0.009817015,0.09015693,0.7480222,0.0003982587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1298982,0.1174761,0.0456344,0.3605089,0.06406263,0.0005116647,0.07497272,0.009371236,0.1975642],"genre_scores_gemma":[0.768356,0.05294554,0.03548837,0.02655191,0.02366219,0.0007037642,0.03683739,0.002103118,0.05335181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682817,"threshold_uncertainty_score":0.1677442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07284009682942479,"score_gpt":0.2955462552007163,"score_spread":0.2227061583712915,"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."}}