{"id":"W2921281695","doi":"10.4230/lipics.approx-random.2019.56","title":"String Matching: Communication, Circuits, and Learning","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"String (physics); Matching (statistics); Computer science; Electronic circuit; Physics; Mathematics; Electrical engineering; Theoretical physics; Engineering","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":[0.0003454165,0.0001979009,0.0002213749,0.0001188299,0.000912831,0.0004936915,0.002691693,0.0001733416,0.000007147359],"category_scores_gemma":[0.00004255638,0.0002266314,0.00006253472,0.00008805977,0.00009372515,0.0007193637,0.007274276,0.0009486773,0.00001930585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005909086,"about_ca_system_score_gemma":0.00008623415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004354105,"about_ca_topic_score_gemma":0.00001633115,"domain_scores_codex":[0.9987099,0.0001705737,0.000125858,0.0007136327,0.00007176989,0.0002083007],"domain_scores_gemma":[0.9973608,0.0001117706,0.0003154857,0.001998394,0.0000905244,0.0001230293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009931517,0.0001034814,0.008680886,0.0001999999,0.0001421353,0.0002718998,0.002115124,0.1320661,0.00007965888,0.8040822,0.0006412262,0.0516074],"study_design_scores_gemma":[0.0005712401,0.00003306974,0.009590412,0.0005098021,0.00004849661,0.00001520332,0.0001930316,0.7993244,0.0000417646,0.1816159,0.007324316,0.0007323732],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1655562,0.0004276716,0.8309346,0.0001012667,0.0002176669,0.0001295494,0.000006736752,0.000226638,0.002399636],"genre_scores_gemma":[0.9932575,0.000994242,0.00452023,0.00002227769,0.0000354646,3.953825e-7,0.00002551245,0.00001086332,0.001133479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8277013,"threshold_uncertainty_score":0.9241759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07923437772593701,"score_gpt":0.2041408928974931,"score_spread":0.1249065151715561,"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."}}