{"id":"W4382866883","doi":"10.1609/socs.v16i1.27277","title":"Core Expansion in Optimization Crosswords","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Lagging; Constraint (computer-aided design); Core (optical fiber); Computer science; Focus (optics); Competition (biology); Function (biology); State (computer science); Mathematical optimization; Optimization problem; Algorithm; Mathematics; Biology; Telecommunications; Physics; Statistics; Geometry","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":[],"consensus_categories":[],"category_scores_codex":[0.0005446163,0.0001019585,0.0001083731,0.0002451127,0.0001098055,0.0001658759,0.001099681,0.00007247469,0.0000299184],"category_scores_gemma":[0.0001570545,0.00008593895,0.00006475049,0.001127609,0.00005581657,0.0004260612,0.0004180951,0.0001897657,0.0000207725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001703122,"about_ca_system_score_gemma":0.00006100805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004306623,"about_ca_topic_score_gemma":0.000002365988,"domain_scores_codex":[0.9983983,0.00001068178,0.0002811257,0.000280984,0.000843622,0.0001853016],"domain_scores_gemma":[0.9992054,0.00008901521,0.000114478,0.0001243642,0.0004231667,0.00004363672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001498502,0.0002248619,0.03657438,0.00002795855,0.0000264704,0.000002162182,0.001165334,0.1561696,0.02898722,0.7705511,0.002231247,0.003889795],"study_design_scores_gemma":[0.001941956,0.0001536541,0.0227269,0.0001798853,0.000002871477,0.00000752703,0.0001298701,0.9194484,0.04112981,0.01303009,0.0009998517,0.0002491497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.940274,0.000004797079,0.004327564,0.01928338,0.0107511,0.0007317205,0.000008700551,0.0003514629,0.02426726],"genre_scores_gemma":[0.9985353,0.0000469784,0.000923364,0.00008919065,0.0001144091,0.00001967741,0.00000449995,0.00000978316,0.000256807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7632788,"threshold_uncertainty_score":0.3504489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03142551140645574,"score_gpt":0.2886787962843634,"score_spread":0.2572532848779077,"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."}}