{"id":"W2983983302","doi":"10.1007/s11192-019-03280-z","title":"Deep and narrow impact: introducing location filtered citation counting","year":2019,"lang":"en","type":"article","venue":"Scientometrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bibliometrics; Citation; Ranking (information retrieval); Computer science; Information retrieval; Biomedicine; Data science; Citation analysis; Rank (graph theory); Scientometrics; Data mining; Mathematics; Library science; Bioinformatics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01018075,0.001523968,0.003091033,0.01961381,0.002058216,0.0095081,0.003808009,0.003143712,0.004820737],"category_scores_gemma":[0.0916492,0.00103852,0.00149357,0.01663026,0.00215653,0.01469066,0.007893325,0.003734723,0.002068852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00219767,"about_ca_system_score_gemma":0.003622463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004323625,"about_ca_topic_score_gemma":0.006132678,"domain_scores_codex":[0.9907234,0.00263001,0.0006505298,0.001688371,0.003585841,0.00072182],"domain_scores_gemma":[0.9469891,0.03016923,0.00481131,0.007465737,0.008980441,0.001584309],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003174594,0.0004286044,0.01897262,0.0005462863,0.0003947121,0.0003258213,0.0006670663,0.0456023,0.00300938,0.3644325,0.01024389,0.5550594],"study_design_scores_gemma":[0.00005577608,0.00009112262,0.003594074,0.0002411746,0.000256925,0.0001783848,0.0002297473,0.4412529,0.003295032,0.5334433,0.01724758,0.0001139836],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02827313,0.001733122,0.9548269,0.001150038,0.0005754488,0.0001479847,0.000748934,0.001667242,0.01087706],"genre_scores_gemma":[0.530212,0.002214035,0.4494583,0.0007961203,0.002514577,0.0003922177,0.001768841,0.001123158,0.01152086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9898192,"threshold_uncertainty_score":0.05384153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3587359287116444,"score_gpt":0.5378950683652745,"score_spread":0.17915913965363,"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."}}