{"id":"W2971155082","doi":"10.1007/978-3-662-59958-7_3","title":"A Study of Three Different Approaches to Point Placement on a Line in an Inexact Model","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Embedding; Heuristic; Pairwise comparison; Algorithm; Point (geometry); Line (geometry); Adversary; Linear programming; Sequence (biology); Function (biology); Set (abstract data type); Theoretical computer science; Mathematical optimization; Mathematics; Artificial intelligence; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002562938,0.001216728,0.001505637,0.001508972,0.001133051,0.004182941,0.004864531,0.004105493,0.008120836],"category_scores_gemma":[0.01233254,0.0009801834,0.001852839,0.003110972,0.003011652,0.005921942,0.003601758,0.003218658,0.001105564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001606409,"about_ca_system_score_gemma":0.001317573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003173744,"about_ca_topic_score_gemma":0.003521202,"domain_scores_codex":[0.9976345,0.0007866821,0.0001021583,0.0003158853,0.001011771,0.0001490136],"domain_scores_gemma":[0.9923653,0.005301136,0.0004749454,0.0009647537,0.0006177265,0.0002760119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001780096,0.0001321994,0.0006530141,0.0004520351,0.00007503959,0.0001405466,0.0004447469,0.5961651,0.001312231,0.305164,0.002203926,0.09307913],"study_design_scores_gemma":[0.00002989021,0.0001858606,0.0002837173,0.00007110983,0.0000386735,0.0001480331,0.000259191,0.9228354,0.001212732,0.07067239,0.004222306,0.00004062499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01691874,0.0009187357,0.9682398,0.0003684633,0.00008040442,0.00005212045,0.00005322442,0.0001750253,0.01319355],"genre_scores_gemma":[0.2915192,0.002294527,0.6888366,0.0001828532,0.0001623581,0.0001766729,0.0002344319,0.0003933524,0.01619996],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008120836,"threshold_uncertainty_score":0.0271669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0940192473809117,"score_gpt":0.2830322310892984,"score_spread":0.1890129837083867,"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."}}