{"id":"W1497689567","doi":"10.1007/978-3-540-45231-7_29","title":"Refined Shared Nearest Neighbors Graph for Combining Multiple Data Clusterings","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; k-nearest neighbors algorithm; Graph; Theoretical computer science; Data mining; Artificial intelligence","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":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001637542,0.0008019752,0.0008219969,0.001104078,0.0006187187,0.001245991,0.01115599,0.0004192751,0.00001382971],"category_scores_gemma":[0.0008628597,0.0007992343,0.0001562015,0.001029411,0.0008202773,0.00174816,0.007256021,0.001177535,0.00002370849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004142908,"about_ca_system_score_gemma":0.0005930678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003711615,"about_ca_topic_score_gemma":0.0001264752,"domain_scores_codex":[0.9928697,0.00005775247,0.0008047213,0.003348568,0.001453573,0.001465724],"domain_scores_gemma":[0.9923727,0.001613783,0.0004050275,0.004753852,0.000483776,0.0003708849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005307958,0.00007956017,0.0000492254,0.0002506947,0.00004818821,0.0001799741,0.0006981762,0.1409462,0.0003565877,0.004393934,0.0004178172,0.8525266],"study_design_scores_gemma":[0.0009093086,0.0002645837,0.00003410552,0.0004140667,0.000007322182,0.00007832742,3.018806e-7,0.9444521,0.0002900115,0.04112396,0.01154185,0.0008840706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001620985,0.0003291705,0.9943068,0.001211406,0.002123588,0.001038564,0.0001054677,0.0003031987,0.0005655647],"genre_scores_gemma":[0.005307243,0.00004620419,0.9915936,0.001873416,0.0003361581,0.00005223749,0.0001039442,0.0001116401,0.000575561],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8516425,"threshold_uncertainty_score":0.9997908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0592828658569979,"score_gpt":0.3098691555839581,"score_spread":0.2505862897269602,"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."}}