{"id":"W2479263369","doi":"10.1016/j.jda.2016.07.001","title":"Reconstructing binary matrices with timetabling constraints","year":2016,"lang":"en","type":"article","venue":"Journal of Discrete Algorithms","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Dimension (graph theory); Binary number; Greedy algorithm; Relaxation (psychology); Matrix (chemical analysis); Mathematical optimization; Mathematics; Computational complexity theory; Computer science; Algorithm; Time complexity; Combinatorics","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":[0.0007408296,0.0008549123,0.0007320135,0.0007211768,0.0003866593,0.001164008,0.0009562593,0.001253973,0.006382558],"category_scores_gemma":[0.007029813,0.0007017436,0.0004602408,0.001337806,0.0006409804,0.002373717,0.001435644,0.001846063,0.00113934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869446,"about_ca_system_score_gemma":0.00113111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003329364,"about_ca_topic_score_gemma":0.004596823,"domain_scores_codex":[0.999265,0.000167955,0.0000459325,0.0001085648,0.0003411626,0.00007133576],"domain_scores_gemma":[0.9962632,0.0023071,0.0002754296,0.0006089386,0.0003875963,0.0001576898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007173801,0.0001794223,0.0008526943,0.0004116025,0.00005814439,0.0004436676,0.0002054941,0.4222082,0.02803517,0.1094586,0.01448748,0.4229422],"study_design_scores_gemma":[0.00003878436,0.00008355734,0.0001274044,0.00002178873,0.000007448848,0.000169318,0.00005377156,0.9439402,0.01034772,0.04166526,0.003525034,0.00001972201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01578853,0.0001461083,0.9812335,0.0002374944,0.00008456807,0.00003430324,0.000190173,0.0003099396,0.001975463],"genre_scores_gemma":[0.1884207,0.0004086984,0.8032405,0.0002221302,0.0001233274,0.0001199275,0.0007620322,0.0002707598,0.00643198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006382558,"threshold_uncertainty_score":0.02135175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348761331051964,"score_gpt":0.2561381153743783,"score_spread":0.2426505020638586,"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."}}