{"id":"W4411548574","doi":"10.22541/au.174889351.10048878/v2","title":"Addressing key challenges in sample handling for high-quality reference genome generation","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Interreg; Agencia Estatal de Investigación; Fundação para a Ciência e a Tecnologia; Vetenskapsrådet; Science for Life Laboratory; HORIZON EUROPE Framework Programme; Ministerio de Ciencia, Innovación y Universidades; Freie Universität Berlin; Natural Sciences and Engineering Research Council of Canada; European Regional Development Fund; New Brunswick Innovation Foundation; European Commission; UK Research and Innovation; Staatssekretariat für Bildung, Forschung und Innovation; Deutsche Forschungsgemeinschaft","keywords":"Key (lock); Sample (material); Computer science; Quality (philosophy); Computational biology; Biology; Computer security; Chemistry; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1058104,0.001718116,0.00249287,0.003679747,0.003883362,0.01357346,0.006391966,0.005255203,0.005909638],"category_scores_gemma":[0.1558454,0.001304492,0.001484138,0.00485001,0.005792624,0.007288794,0.01009456,0.00695755,0.009037579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002651046,"about_ca_system_score_gemma":0.01273313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005760072,"about_ca_topic_score_gemma":0.009445565,"domain_scores_codex":[0.9262481,0.03779338,0.007424973,0.008035932,0.01818363,0.002313902],"domain_scores_gemma":[0.876533,0.05178772,0.0064118,0.02930561,0.03338583,0.002576036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008198993,0.0003833713,0.01783312,0.01161877,0.0004999146,0.002426845,0.01248384,0.006737689,0.1532426,0.07712443,0.1256219,0.5912076],"study_design_scores_gemma":[0.00008292373,0.0002710493,0.009317944,0.004412782,0.000221937,0.002296603,0.005468513,0.004315986,0.07490323,0.09306628,0.8053219,0.0003208015],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02450277,0.02199214,0.8828881,0.04039324,0.006021014,0.001564418,0.003955052,0.004653311,0.01402996],"genre_scores_gemma":[0.05691292,0.01032219,0.9008175,0.009154824,0.001933172,0.002328016,0.01064317,0.00368343,0.004204696],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1058104,"threshold_uncertainty_score":0.5595857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2885773541651048,"score_gpt":0.3739911045518094,"score_spread":0.08541375038670462,"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."}}