{"id":"W4387029309","doi":"10.5376/cmb.2023.13.0003","title":"Computational Molecular Biology Interdisciplinary Technological Integration and New Advances","year":2023,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Systems biology; Computational biology; Computer science; Biology; Computational model; Data science; Management science; Nanotechnology; Artificial intelligence; Engineering; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008553622,0.0013378,0.00177393,0.003227449,0.0009758552,0.006465548,0.003599397,0.002841604,0.008420579],"category_scores_gemma":[0.01312265,0.0008705038,0.001965787,0.00363102,0.003757455,0.01072456,0.006101294,0.006557467,0.003026786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002809787,"about_ca_system_score_gemma":0.005268134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001751762,"about_ca_topic_score_gemma":0.001255887,"domain_scores_codex":[0.9953704,0.001676933,0.000261488,0.0006329744,0.001736177,0.0003219195],"domain_scores_gemma":[0.9895492,0.005767872,0.0003590767,0.001668559,0.00201458,0.0006408078],"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":[0.00007911554,0.0001024963,0.001239051,0.002172976,0.0001974399,0.0002385997,0.0004278251,0.01076444,0.00185234,0.6681509,0.03279996,0.2819748],"study_design_scores_gemma":[0.00002958578,0.00004780094,0.0005621157,0.001177409,0.00007881629,0.0004748772,0.0002631874,0.02595663,0.001160107,0.354267,0.6159071,0.00007531985],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.009613135,0.5082193,0.263559,0.1155576,0.008677322,0.00015833,0.0007389502,0.001535422,0.09194095],"genre_scores_gemma":[0.09976759,0.5399764,0.3131053,0.01481914,0.01304732,0.0004044731,0.001930602,0.0008438433,0.01610537],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008553622,"threshold_uncertainty_score":0.04523641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01887304333335217,"score_gpt":0.3501770350842353,"score_spread":0.3313039917508832,"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."}}