{"id":"W2790020036","doi":"10.1007/978-3-319-94776-1_36","title":"Reconfiguring Spanning and Induced Subgraphs","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Japan Science and Technology Agency; Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Control reconfiguration; Reachability; Induced subgraph isomorphism problem; Combinatorics; Subgraph isomorphism problem; Matching (statistics); Graph factorization; Independent set; Graph; Mathematics; Induced subgraph; Clique; Computer science; Split graph; Distance-hereditary graph; Theoretical computer science; Discrete mathematics; Line graph; Graph power; Vertex (graph theory); Pathwidth; Voltage 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.002474184,0.0004753602,0.0004899558,0.001532537,0.00041777,0.001239029,0.00491354,0.0003070225,0.000005762626],"category_scores_gemma":[0.0004238103,0.0004540847,0.00008974568,0.002554461,0.001130101,0.0008233419,0.008288119,0.00160778,0.00001208587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001729936,"about_ca_system_score_gemma":0.0005145195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005829562,"about_ca_topic_score_gemma":0.00004152769,"domain_scores_codex":[0.9949618,0.0002013356,0.0004528576,0.002374416,0.0009125259,0.001097052],"domain_scores_gemma":[0.9964957,0.0006122179,0.0002264863,0.00205994,0.0003185132,0.0002871784],"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":[0.00001387105,0.00005028597,0.004588519,0.0001238306,0.00001668474,0.00009945791,0.004165464,0.01899044,0.005117228,0.002844776,0.00000437762,0.9639851],"study_design_scores_gemma":[0.0002293123,0.0001541416,0.004979793,0.0004300779,0.000002605364,0.00006381991,8.951946e-7,0.5424814,0.03155238,0.4194638,0.00001671446,0.0006249763],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2368894,0.0002916524,0.7601929,0.0004803992,0.001564752,0.000336553,0.000001248209,0.0001864579,0.00005662431],"genre_scores_gemma":[0.6503358,0.00002371746,0.349106,0.0002820814,0.0002170378,0.00001856753,7.889932e-7,0.00001533113,6.524824e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9633601,"threshold_uncertainty_score":0.9997978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03660469968966788,"score_gpt":0.3142294430628079,"score_spread":0.27762474337314,"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."}}