{"id":"W2002041176","doi":"10.1007/s10618-006-0046-6","title":"Relational peculiarity-oriented mining","year":2007,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Relevance (law); Computer science; Identification (biology); Relational database; Data mining; Task (project management); Focus (optics); Information retrieval; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0009250945,0.0001414151,0.0001485239,0.0001069764,0.0002811787,0.0002413921,0.0007222263,0.00007209503,0.000004573147],"category_scores_gemma":[0.0001700983,0.0001232676,0.00002615422,0.0003219213,0.00007649294,0.001739471,0.001148527,0.0001060172,0.00002214306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001745806,"about_ca_system_score_gemma":0.00007732175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001218969,"about_ca_topic_score_gemma":0.00005854268,"domain_scores_codex":[0.9986501,0.00003635687,0.0002509335,0.0005774126,0.000171011,0.0003141657],"domain_scores_gemma":[0.9986705,0.0002966482,0.00007345919,0.000811514,0.00004193338,0.0001059317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006186606,0.0004351032,0.06086214,0.0000740198,0.00009320831,0.0001724428,0.01401851,0.000006931289,0.0001457199,0.2259057,0.05364047,0.6445839],"study_design_scores_gemma":[0.003401882,0.0004790646,0.2586576,0.0005659459,0.0001058168,0.0003383658,0.006996656,0.242052,0.0002076184,0.002266536,0.4826951,0.002233404],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2750052,0.002471363,0.6597015,0.0002984719,0.001450862,0.0001208639,0.000155266,0.0001862794,0.06061018],"genre_scores_gemma":[0.8029202,0.0000524613,0.1942894,0.0001984453,0.0004062271,0.000002803895,0.0006452779,0.00001373635,0.001471466],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6423506,"threshold_uncertainty_score":0.5026708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0726774133759716,"score_gpt":0.2994970904899545,"score_spread":0.2268196771139829,"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."}}