{"id":"W4297018352","doi":"10.32920/14638791.v2","title":"Efficient computation of spaced seeds","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristics; Heuristic; Computer science; Software; Computation; Quadratic equation; Algorithm; Speedup; Range (aeronautics); Theoretical computer science; Artificial intelligence; Mathematics; Parallel computing; Programming language; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002624018,0.0001430639,0.0002271705,0.0001499062,0.00007728067,0.00007159851,0.001079578,0.00007318347,0.0001132974],"category_scores_gemma":[0.0000101373,0.0001260866,0.00009144592,0.0002063889,0.00002131101,0.00003722237,0.006388795,0.0002909692,0.000009761894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005116594,"about_ca_system_score_gemma":0.0001271728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001642636,"about_ca_topic_score_gemma":0.000001094627,"domain_scores_codex":[0.9985825,0.00007706112,0.0002695417,0.0004493905,0.0004837284,0.000137796],"domain_scores_gemma":[0.9988385,0.00004748917,0.000235559,0.0007442896,0.0000826454,0.00005154032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005509251,0.0001859543,0.00006873291,0.0001144547,0.00002578552,0.000008666232,0.0006318582,0.9410339,0.0002102854,0.02398271,0.003438489,0.03029368],"study_design_scores_gemma":[0.0001291521,0.00003606586,0.001023831,0.00003648773,0.000004614371,0.000001999039,0.00002919526,0.9953054,0.0002582269,0.001995378,0.001031646,0.0001479736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01317358,0.0001425893,0.9802296,0.0002362306,0.001202755,0.0002076334,0.00002145737,0.0001564468,0.004629715],"genre_scores_gemma":[0.6548976,0.000009237639,0.3444438,0.00008678142,0.00005367254,0.00002457933,0.00009176278,0.0000113072,0.0003812874],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.641724,"threshold_uncertainty_score":0.7963176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02081935101985498,"score_gpt":0.2787534795356401,"score_spread":0.2579341285157851,"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."}}