{"id":"W3199722554","doi":"10.4230/lipics.isaac.2021.44","title":"Exact and Approximation Algorithms for Many-To-Many Point Matching in the Plane","year":2021,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Combinatorics; Bipartite graph; Mathematics; Euclidean geometry; Matching (statistics); Planar graph; Planar; Approximation algorithm; Plane (geometry); Cover (algebra); Discrete mathematics; Graph; Geometry; Computer science","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001669107,0.0004481632,0.000503355,0.0004854574,0.0003259096,0.001652774,0.001173047,0.000283201,0.000004683483],"category_scores_gemma":[0.0001226743,0.0003801812,0.0002073393,0.0003807887,0.00003398394,0.001177708,0.001064492,0.0005862789,0.00001045991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141159,"about_ca_system_score_gemma":0.0001546487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000300438,"about_ca_topic_score_gemma":0.00002748483,"domain_scores_codex":[0.9971176,0.00008804589,0.001152291,0.0005178489,0.0005867489,0.0005374643],"domain_scores_gemma":[0.9978955,0.0004090549,0.0004765528,0.0007855355,0.0002988482,0.000134462],"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":[0.0003719373,0.001624656,0.0007709099,0.01101603,0.0007282709,0.0000470833,0.3307416,0.1518548,0.0003489015,0.1495895,0.008439923,0.3444663],"study_design_scores_gemma":[0.001774915,0.0002060492,0.001584434,0.000465819,0.0000400871,0.0001187428,0.003016946,0.9715618,0.0004396785,0.01313014,0.006941354,0.0007199816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1182514,0.00009100526,0.8759266,0.001401407,0.001086769,0.002600018,0.0002795452,0.00008173988,0.0002815638],"genre_scores_gemma":[0.5230787,0.00006465881,0.4684097,0.003027006,0.0004553308,0.001167329,0.003660395,0.00004402681,0.00009282699],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8197071,"threshold_uncertainty_score":0.999865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379987069991624,"score_gpt":0.2771483765634007,"score_spread":0.2533485058634845,"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."}}