{"id":"W1509195019","doi":"10.18438/b8cs37","title":"Use Google Scholar, Scopus and Web of Science for Comprehensive Citation Tracking","year":2007,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Citation; Scopus; Web of science; Citation analysis; Sample size determination; Impact factor; Stratified sampling; Sample (material); Computer science; Library science; Information retrieval; World Wide Web; Psychology; Medicine; MEDLINE; Mathematics; Statistics; Meta-analysis; Physics; Internal medicine; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0193036,0.003502526,0.004608638,0.1337466,0.001977429,0.008326458,0.003166479,0.002309017,0.1308713],"category_scores_gemma":[0.1142311,0.001205694,0.002549815,0.1460016,0.0006962041,0.01121109,0.006658721,0.001835762,0.08486053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002923548,"about_ca_system_score_gemma":0.02242097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01505783,"about_ca_topic_score_gemma":0.01463971,"domain_scores_codex":[0.9790217,0.001783378,0.007836192,0.001738031,0.008512014,0.001108742],"domain_scores_gemma":[0.8629361,0.0259911,0.02731979,0.01112128,0.06732181,0.00530984],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004438554,0.0001854191,0.008846786,0.03476675,0.0004768643,0.0006368743,0.0007599061,0.0005264466,0.001247786,0.005907451,0.5825558,0.3636461],"study_design_scores_gemma":[0.0004229557,0.0002395627,0.04197863,0.01165785,0.0006678093,0.0004414564,0.0009210215,0.001627658,0.002447334,0.01017764,0.928987,0.0004309847],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.008451122,0.007261377,0.009712554,0.005718612,0.001725428,0.009678361,0.8394973,0.02407376,0.09388141],"genre_scores_gemma":[0.03805972,0.02076765,0.1574838,0.002834609,0.001629286,0.03415648,0.6709574,0.0115356,0.06257545],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9806964,"threshold_uncertainty_score":0.4378081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4677110181043493,"score_gpt":0.525365212743652,"score_spread":0.05765419463930271,"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."}}