{"id":"W1971324060","doi":"10.1007/pl00011676","title":"Parallel and Sequential Algorithms for Data Mining Using Inductive Logic","year":2001,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"IBM (Canada); Queen's University","funders":"","keywords":"Computer science; Inductive logic programming; Algorithm; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005356062,0.0001110167,0.00015687,0.0001251124,0.000246501,0.0004164954,0.0003015587,0.00006437734,9.032349e-7],"category_scores_gemma":[0.00005003278,0.000094148,0.00001514681,0.0001698041,0.00002857848,0.003693938,0.0002915363,0.00007628032,0.00000977358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001756509,"about_ca_system_score_gemma":0.00004428105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008292831,"about_ca_topic_score_gemma":0.000002223913,"domain_scores_codex":[0.999189,0.00005162134,0.0002924807,0.0001897822,0.0001009419,0.0001762227],"domain_scores_gemma":[0.9993036,0.00006271758,0.000150479,0.0002866342,0.0001241897,0.00007240928],"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":[0.00002632625,0.00005252366,0.002113991,0.0004669481,0.0001008853,0.000004379678,0.02022727,0.001253505,0.00004967396,0.06008751,0.002996197,0.9126208],"study_design_scores_gemma":[0.000436134,0.00004600847,0.0001632987,0.00003963679,0.000006452467,0.0001089235,0.0004081414,0.9037437,0.000002264335,0.00008082942,0.09484187,0.0001227187],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007391149,0.0007349274,0.9880199,0.00008325535,0.0006593048,0.000259443,0.00001115283,0.0000775071,0.002763373],"genre_scores_gemma":[0.6858633,0.0002329166,0.3114687,0.0002321271,0.001147605,0.00006144508,0.000215113,0.00001726151,0.0007615275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9124981,"threshold_uncertainty_score":0.4016275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09966565189006675,"score_gpt":0.3377073262655644,"score_spread":0.2380416743754977,"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."}}