{"id":"W2038878443","doi":"10.1016/s0304-3975(03)00422-5","title":"Finding hidden independent sets in interval graphs","year":2003,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"University of Waterloo","keywords":"Interval graph; Interval (graph theory); Set (abstract data type); Computer science; Graph; Theoretical computer science; Mathematics; Algorithm; Discrete mathematics; Combinatorics; Chordal graph","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001816615,0.0009709361,0.001687775,0.002159552,0.001039958,0.002535172,0.003284011,0.001544279,0.004452765],"category_scores_gemma":[0.01810997,0.001629966,0.0015953,0.002102916,0.001622232,0.005461043,0.001947449,0.003796511,0.0005980896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007911421,"about_ca_system_score_gemma":0.0005735133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008287375,"about_ca_topic_score_gemma":0.0009241755,"domain_scores_codex":[0.9986238,0.0003881357,0.00007912898,0.0004037083,0.000319587,0.0001855539],"domain_scores_gemma":[0.9731435,0.02205038,0.001887727,0.001570903,0.0006910245,0.0006564744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00228093,0.0009916448,0.02051171,0.001130697,0.0006974765,0.001255879,0.001891794,0.3077142,0.01003604,0.4273719,0.00961332,0.2165044],"study_design_scores_gemma":[0.00009492882,0.00007937389,0.001767961,0.00006359904,0.0001139818,0.0001305243,0.0001536341,0.4272465,0.001861708,0.5676482,0.0008117703,0.00002776198],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2590762,0.0007572423,0.7333488,0.0005959453,0.00007000132,0.0001053135,0.0006696049,0.0006378097,0.004739158],"genre_scores_gemma":[0.84698,0.0006520783,0.1466171,0.0001540587,0.0001629315,0.0001794533,0.00179231,0.000183175,0.003278837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004452765,"threshold_uncertainty_score":0.01489598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100445987876518,"score_gpt":0.2695621984548484,"score_spread":0.2585577385760832,"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."}}