{"id":"W2101380104","doi":"10.7771/1932-6246.1142","title":"Human Performance on Hard Non-Euclidean Graph Problems: Vertex Cover","year":2012,"lang":"en","type":"article","venue":"The Journal of Problem Solving","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vertex cover; Edge cover; Vertex (graph theory); Euclidean geometry; Computer science; Graph; Cover (algebra); Combinatorics; Covering problems; Independent set; Set (abstract data type); Mathematics; Mathematical optimization; Theoretical computer science","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":[0.001461706,0.0001213361,0.0001486635,0.0001551153,0.0003489798,0.0001055589,0.0005269597,0.00004011845,0.00007579403],"category_scores_gemma":[0.00002157168,0.00008077334,0.00007952808,0.000250364,0.00004492328,0.001280367,0.00008227159,0.0003209131,0.00005327482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000649213,"about_ca_system_score_gemma":0.00005097808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005201726,"about_ca_topic_score_gemma":0.000001296365,"domain_scores_codex":[0.9987922,0.00009035876,0.0003966698,0.00008429253,0.000354962,0.0002815686],"domain_scores_gemma":[0.9990057,0.00007318633,0.000428746,0.0002477855,0.0001384203,0.0001061079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000176724,0.00120224,0.1363442,0.0005693758,0.0006593927,0.0000197283,0.08575016,0.3631018,0.07342493,0.05614812,0.04324465,0.2393587],"study_design_scores_gemma":[0.005926478,0.003118547,0.751193,0.003073227,0.0002991009,0.001874811,0.001053896,0.1995843,0.01248346,0.01067927,0.008782998,0.001930857],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.452493,0.0002246641,0.5329442,0.001530957,0.001206237,0.0003854237,7.87252e-7,0.00008114659,0.01113357],"genre_scores_gemma":[0.9864794,0.00007873744,0.01279766,0.0002913755,0.000113618,0.00000127573,2.40644e-7,0.000009453619,0.0002282339],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6148489,"threshold_uncertainty_score":0.3293841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199978733259338,"score_gpt":0.2397387067056269,"score_spread":0.2197408333796931,"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."}}