{"id":"W3197560144","doi":"10.1101/2021.09.07.459345","title":"Supervised Promoter Recognition: A benchmark framework","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria","keywords":"Benchmark (surveying); Benchmarking; Deep learning; Computer science; Machine learning; Artificial intelligence; Documentation; Process (computing); Source code; Reliability (semiconductor); Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004009777,0.001989078,0.001028914,0.002076125,0.0008006167,0.001839383,0.004452905,0.00304535,0.002983766],"category_scores_gemma":[0.008406544,0.0005410157,0.001592919,0.002102293,0.001049796,0.00183756,0.001747629,0.002025695,0.001986855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002194504,"about_ca_system_score_gemma":0.002470078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01285264,"about_ca_topic_score_gemma":0.01219587,"domain_scores_codex":[0.9972193,0.0007330449,0.00017078,0.0009453144,0.0006866201,0.0002448271],"domain_scores_gemma":[0.9964464,0.001193752,0.000210933,0.0009262165,0.001029613,0.000192973],"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":[0.0007787733,0.0007026562,0.005986226,0.0009626775,0.0003077709,0.0003274989,0.00008283234,0.7126765,0.01148081,0.01918088,0.05597386,0.1915396],"study_design_scores_gemma":[0.00005827735,0.0001704148,0.00085423,0.00004183269,0.00002396984,0.0001046794,0.00003561404,0.9665578,0.01117803,0.0119553,0.008991686,0.00002816558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1978468,0.00904368,0.6901582,0.002429931,0.000826697,0.0007456679,0.04757181,0.03154106,0.01983609],"genre_scores_gemma":[0.3722123,0.002201188,0.4768889,0.0006654473,0.0002041818,0.0011264,0.1357058,0.001383951,0.009611863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01285264,"threshold_uncertainty_score":0.02555567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01061196382810806,"score_gpt":0.2083665337778507,"score_spread":0.1977545699497427,"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."}}