{"id":"W2293919411","doi":"10.29173/cais549","title":"Collecting Bibliographic References: A Bibliometric Analysis of CiteULike's Collection as Grounds for In-Depth Interviews","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; Web of science; Sociology; Humanities; Political science; Art; Computer science; MEDLINE","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.02105057,0.0005167641,0.001118228,0.09400995,0.005062376,0.005883977,0.00127394,0.001069682,0.004046724],"category_scores_gemma":[0.0769557,0.0003409605,0.0007331452,0.1117886,0.002141293,0.004922562,0.004169878,0.0006614078,0.001111589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006507188,"about_ca_system_score_gemma":0.009704933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009028775,"about_ca_topic_score_gemma":0.01883341,"domain_scores_codex":[0.9757193,0.00918252,0.003100357,0.001117268,0.009893346,0.0009871448],"domain_scores_gemma":[0.9274802,0.03791615,0.005334712,0.003599138,0.02475681,0.0009131073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002118529,0.0001957293,0.08183652,0.006127589,0.0002448088,0.001041595,0.3283257,0.0008574619,0.01157112,0.0368803,0.02169949,0.5110078],"study_design_scores_gemma":[0.00006255531,0.0002783509,0.2426386,0.003966611,0.0004355526,0.001086963,0.3536262,0.003551492,0.01086068,0.01865501,0.3645488,0.0002892198],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7775832,0.006064553,0.05307657,0.003394065,0.0004003823,0.005929256,0.01853317,0.0007526674,0.1342662],"genre_scores_gemma":[0.8759063,0.004983058,0.08110178,0.0004645259,0.0002823288,0.008218347,0.01079013,0.0003490901,0.0179043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9059901,"threshold_uncertainty_score":0.1113274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0674813722550687,"score_gpt":0.3005287766883557,"score_spread":0.233047404433287,"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."}}