{"id":"W2257535843","doi":"10.1007/s10601-016-9238-x","title":"ℚ-bounds consistency for the spread constraint with variable mean","year":2016,"lang":"en","type":"article","venue":"Constraints","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variable (mathematics); Constraint (computer-aided design); Mathematics; Consistency (knowledge bases); Piecewise; Local consistency; Algorithm; Extension (predicate logic); Function (biology); Mathematical optimization; Computer science; Constraint satisfaction; Statistics; Discrete mathematics","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":[0.01304685,0.001953181,0.004053639,0.002751893,0.002601883,0.006517561,0.008074513,0.003815424,0.01038254],"category_scores_gemma":[0.08957387,0.002254972,0.003303595,0.006548978,0.004635072,0.01807746,0.009396043,0.0106802,0.00167513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003093751,"about_ca_system_score_gemma":0.005177681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004962429,"about_ca_topic_score_gemma":0.003722791,"domain_scores_codex":[0.9834425,0.005449997,0.001101577,0.003923732,0.004574212,0.001508109],"domain_scores_gemma":[0.9119903,0.06245442,0.003170658,0.0134398,0.007392264,0.001552578],"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":[0.0007722436,0.000234866,0.002040977,0.0005975286,0.0003535243,0.0002436309,0.0005931334,0.2151393,0.002134422,0.6646003,0.02044292,0.09284728],"study_design_scores_gemma":[0.00007136581,0.00004676955,0.0002769506,0.00007788397,0.00006566918,0.00009579737,0.00005490624,0.3796548,0.001441525,0.6148165,0.003354538,0.00004343479],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01254648,0.0007719257,0.9772847,0.001870251,0.00020046,0.0000858631,0.0004724334,0.0005370858,0.00623086],"genre_scores_gemma":[0.3858498,0.001400538,0.5908739,0.002080905,0.001429699,0.0006364134,0.002485177,0.001602204,0.01364132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01304685,"threshold_uncertainty_score":0.06899911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114691847701545,"score_gpt":0.2322014977262077,"score_spread":0.2110545792491923,"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."}}