{"id":"W2604406275","doi":"10.1111/1556-4029.13504","title":"An Optimized DNA Analysis Workflow for the Sampling, Extraction, and Concentration of DNA obtained from Archived Latent Fingerprints","year":2017,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dawson College","funders":"National Institute of Justice; Virginia Commonwealth University","keywords":"DNA profiling; DNA extraction; DNA; Fingerprint (computing); STR analysis; Workflow; Sampling (signal processing); Computational biology; Microsatellite; Chromatography; Computer science; Polymerase chain reaction; Biology; Genetics; Database; Artificial intelligence; Chemistry; Allele; Gene; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0037608,0.001124881,0.0009345778,0.002036378,0.0009188902,0.001654457,0.001441062,0.001163594,0.002431749],"category_scores_gemma":[0.005965119,0.0007220862,0.0008093652,0.001557629,0.0007766985,0.0009246921,0.0009121886,0.001529187,0.003131777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000497844,"about_ca_system_score_gemma":0.002513283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297298,"about_ca_topic_score_gemma":0.003305297,"domain_scores_codex":[0.9962386,0.0005727063,0.0004273943,0.001051675,0.001462452,0.0002471453],"domain_scores_gemma":[0.9969552,0.0006747154,0.0004900728,0.0005220085,0.001217927,0.0001400306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002324897,0.0002593816,0.004050944,0.0004488234,0.00004111604,0.0001618994,0.0003253434,0.0006430456,0.9467263,0.0004954963,0.0006618363,0.04595331],"study_design_scores_gemma":[0.00004321526,0.00107696,0.01195909,0.0002123105,0.0001505099,0.001426041,0.0002734416,0.006611402,0.9575025,0.0007408279,0.01986422,0.0001394056],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.137155,0.001722074,0.8473346,0.0003000923,0.0002130893,0.003887286,0.003542302,0.003742799,0.002102794],"genre_scores_gemma":[0.06703039,0.001383675,0.9220126,0.0002833484,0.00004573748,0.002017721,0.0040972,0.0003672535,0.002762015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0037608,"threshold_uncertainty_score":0.01988924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04534923502877156,"score_gpt":0.3604791812814425,"score_spread":0.315129946252671,"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."}}