{"id":"W2402343526","doi":"10.1609/aaai.v26i1.8147","title":"From Streamlined Combinatorial Search to Efficient Constructive Procedures","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"graph theory and CDMA systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Combinatorial explosion; Constructive; Constraint satisfaction problem; Computation; Combinatorial search; Combinatorial optimization; Computer science; Mathematics; Theoretical computer science; Series (stratigraphy); Algebra over a field; Combinatorics; Search algorithm; Algorithm; Artificial intelligence; Pure mathematics; Beam search","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002231377,0.000216548,0.0002882683,0.00008652129,0.0001191805,0.0001236399,0.0005619476,0.000101192,0.0001700288],"category_scores_gemma":[0.0003570333,0.0001783402,0.0001062181,0.0006044948,0.0001774544,0.00006632999,0.000128062,0.0002751026,0.0001294759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004966008,"about_ca_system_score_gemma":0.0001027262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003764513,"about_ca_topic_score_gemma":0.00001166008,"domain_scores_codex":[0.99848,0.00002094973,0.000428246,0.0003517024,0.0004103907,0.0003087361],"domain_scores_gemma":[0.9988317,0.0001010812,0.00007304575,0.0002003642,0.0006700995,0.0001236916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001520044,0.0001322337,0.0001385328,0.00009937665,0.00006514175,0.000001671687,0.003430245,0.003792756,0.2939672,0.6911204,0.0002134918,0.006886934],"study_design_scores_gemma":[0.00004382104,0.0000680325,0.00009753028,0.0003326998,0.00001716691,0.000003431503,0.004544082,0.01386394,0.9102246,0.07055064,0.00005181912,0.000202235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871877,0.00003916837,0.0007618819,0.0003182131,0.001484065,0.000378412,0.00005620534,0.0001165011,0.00965786],"genre_scores_gemma":[0.9994528,0.000008840355,0.0002034366,0.00003328858,0.0001913627,0.00003093035,0.000002226192,0.00002172734,0.00005540272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6205698,"threshold_uncertainty_score":0.7272501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03226215235074346,"score_gpt":0.2628247765697286,"score_spread":0.2305626242189852,"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."}}