{"id":"W2070894968","doi":"10.4230/lipics.stacs.2014.300","title":"From Small Space to Small Width in Resolution","year":2014,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Vetenskapsrådet; Kungliga Tekniska Högskolan; University of Toronto; European Commission","keywords":"Resolution (logic); Upper and lower bounds; Space (punctuation); Proof complexity; Mathematics; Simple (philosophy); Polynomial; Measure (data warehouse); PSPACE; Discrete mathematics; Product (mathematics); Calculus (dental); Algorithm; Computer science; Combinatorics; Computational complexity theory; Mathematical proof; Mathematical analysis; Geometry; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001080548,0.0003699121,0.0004775315,0.0003297785,0.0001934958,0.0005620641,0.00151816,0.0002443307,0.000008436218],"category_scores_gemma":[0.0001948147,0.0003298534,0.0001723925,0.0004906552,0.00005356345,0.0007784694,0.0005662786,0.0003221949,0.0004694532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001721395,"about_ca_system_score_gemma":0.00006332606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006349184,"about_ca_topic_score_gemma":0.0008028981,"domain_scores_codex":[0.9972437,0.00009297887,0.0009784366,0.0004252119,0.0003721589,0.0008874949],"domain_scores_gemma":[0.9979126,0.0001730066,0.0003444419,0.001086901,0.0001834122,0.0002996207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003111566,0.001073061,0.04022079,0.0009908096,0.0002464142,0.00002746532,0.08607465,0.001846201,0.0003410955,0.6434833,0.01316502,0.21222],"study_design_scores_gemma":[0.005439208,0.001026653,0.006826412,0.0001753706,0.0000335183,0.00003877617,0.001262571,0.2595016,0.001360255,0.03547494,0.687332,0.001528622],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07943147,0.00003695934,0.9093125,0.000678424,0.001523585,0.001155853,0.0000298574,0.0002886649,0.007542645],"genre_scores_gemma":[0.9156415,0.000006429319,0.08197574,0.001190846,0.0003704661,0.000147028,0.0001405444,0.00003336858,0.0004941038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.83621,"threshold_uncertainty_score":0.9999154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214687427575543,"score_gpt":0.2365808230029513,"score_spread":0.2144339487271959,"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."}}