{"id":"W4396844948","doi":"10.1186/s13104-024-06791-y","title":"An inversion-based clustering approach for complex clusters","year":2024,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Similarity measure; Profiling (computer programming); Feature (linguistics); Similarity (geometry); Ranking (information retrieval); Artificial intelligence; Inversion (geology); Machine learning; Pattern recognition (psychology)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003170795,0.0002301805,0.0002374267,0.0008700475,0.0005951885,0.001339252,0.002440523,0.0001194418,0.00001956658],"category_scores_gemma":[0.0007835893,0.0002110977,0.0001393811,0.001527808,0.0003014356,0.001016133,0.0009064553,0.0005943116,0.00007274588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003401529,"about_ca_system_score_gemma":0.0005769769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000121504,"about_ca_topic_score_gemma":0.0000354701,"domain_scores_codex":[0.9953845,0.0005153974,0.0002916191,0.001162922,0.001404183,0.00124143],"domain_scores_gemma":[0.9944246,0.00344889,0.00002604684,0.001254201,0.0004204335,0.0004258425],"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":[0.0003937191,0.000610568,0.0005339387,0.004741553,0.00009617981,0.0001291203,0.002173554,0.4175628,0.04066,0.009742873,0.006425997,0.5169296],"study_design_scores_gemma":[0.0004467991,0.0003765759,0.0001397275,0.00007653889,0.000002042266,0.000007847152,0.0001229975,0.9924547,0.001812461,0.001092142,0.003218952,0.000249222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001147383,0.0002510283,0.9954252,0.0009088271,0.0002218778,0.001005972,0.00002060104,0.0006151304,0.0004039752],"genre_scores_gemma":[0.3251397,0.00000631942,0.6739201,0.00006918178,0.0002444437,0.0002903665,0.00004030929,0.0000463517,0.0002432377],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5748919,"threshold_uncertainty_score":0.9996974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3002684837542525,"score_gpt":0.4587841597762987,"score_spread":0.1585156760220462,"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."}}