{"id":"W2805783523","doi":"10.1007/978-3-319-92058-0_8","title":"A Comparison of Knee Strategies for Hierarchical Spatial Clustering","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Hierarchical clustering; Computer science; Cluster analysis; Medoid; Centroid; Dendrogram; Cluster (spacecraft); Data mining; Artificial intelligence; Complete linkage; Pattern recognition (psychology); Population","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":[],"consensus_categories":[],"category_scores_codex":[0.004828253,0.001625265,0.002472989,0.003074969,0.001510511,0.00388591,0.004135366,0.002172679,0.008729295],"category_scores_gemma":[0.01560159,0.0007491912,0.001472419,0.0046829,0.001112253,0.004668908,0.003347022,0.00155491,0.002481801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835611,"about_ca_system_score_gemma":0.002942778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01039831,"about_ca_topic_score_gemma":0.01610983,"domain_scores_codex":[0.9973179,0.0009249962,0.0001898781,0.0002929468,0.0009646457,0.0003096872],"domain_scores_gemma":[0.9917942,0.004347165,0.0003053912,0.001090916,0.001977121,0.0004851742],"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":[0.00163973,0.0005319232,0.00127593,0.0008099537,0.0003384694,0.00006997692,0.0005322985,0.1347672,0.003540269,0.07199597,0.01522378,0.7692745],"study_design_scores_gemma":[0.0003043412,0.0006770859,0.001483369,0.0001672912,0.0002422845,0.0002273915,0.0009835409,0.9176179,0.003789264,0.06695358,0.007457228,0.00009667918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0314624,0.004652618,0.9493369,0.0003575373,0.0002556279,0.0002842674,0.0003322301,0.002379099,0.01093935],"genre_scores_gemma":[0.2685302,0.003273537,0.7156395,0.0003644202,0.0001615477,0.0003166662,0.001576914,0.001436971,0.008700261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01039831,"threshold_uncertainty_score":0.02920246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04336293284831568,"score_gpt":0.3482684205343744,"score_spread":0.3049054876860587,"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."}}