{"id":"W6906556231","doi":"10.17632/y28j8mgf8k","title":"Cubic Knapsack Problem Instances","year":2025,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université du Québec à Montréal","funders":"","keywords":"Knapsack problem; Set (abstract data type); Heuristic; Data set; Dynamic programming","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","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001250604,0.0009230061,0.001042572,0.0006150524,0.000225719,0.0003345064,0.01028274,0.0005766139,0.001854119],"category_scores_gemma":[0.0005175282,0.0008838979,0.00007961223,0.001579496,0.0001977598,0.0009369751,0.006517837,0.001325056,0.007747329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003307424,"about_ca_system_score_gemma":0.001429949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00093983,"about_ca_topic_score_gemma":0.004336006,"domain_scores_codex":[0.9942835,0.0003224746,0.0009328644,0.002310928,0.001165494,0.0009847606],"domain_scores_gemma":[0.9865341,0.0001615651,0.0005999357,0.01229883,0.0001935555,0.0002119753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007095768,0.0002225977,0.000002963209,0.0007987042,0.0003702401,0.00008046971,0.000006558267,0.000004314441,0.00001771178,0.00004575746,0.9972271,0.00115267],"study_design_scores_gemma":[0.0005663259,0.0000575839,0.000001357698,0.0008110237,0.0004799627,0.00001381991,0.00002394278,0.00004788894,0.00001906317,0.0002002632,0.9969294,0.0008493791],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000111257,0.003605879,0.000008023732,0.0001307304,0.001176177,0.0009894018,0.9901262,0.0003008605,0.003661595],"genre_scores_gemma":[0.000001012498,0.002719913,0.001361985,0.000328756,0.0004336375,0.00008107555,0.9922976,0.00008065096,0.002695295],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009987901,"threshold_uncertainty_score":0.9993612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06570413452546865,"score_gpt":0.3460734610495866,"score_spread":0.2803693265241179,"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."}}