{"id":"W2966009755","doi":"10.1101/720219","title":"Liquid biopsies for omics-based analysis in sentinel mussels","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Aquatic Invertebrate Ecology and Behavior","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Armand Frappier Museum; Institut National de la Recherche Scientifique","funders":"Terres Australes et Antarctiques Françaises; Institut Polaire Français Paul Emile Victor","keywords":"Sampling (signal processing); Nucleic acid; Metagenomics; Liquid biopsy; Biopsy; Biology; Computational biology; Digital polymerase chain reaction; Computer science; Pathology; Medicine; Polymerase chain reaction; Biochemistry; Genetics","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":[0.0006656262,0.0005346956,0.0003270872,0.0007279904,0.0004043676,0.0006931488,0.0002963126,0.0005704983,0.001470379],"category_scores_gemma":[0.0005375156,0.0002604277,0.0003320558,0.0003081982,0.0003724601,0.0003748052,0.0007298128,0.0005317861,0.0009909765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001680647,"about_ca_system_score_gemma":0.0002787901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007070411,"about_ca_topic_score_gemma":0.001441292,"domain_scores_codex":[0.999627,0.00008377825,0.00002535213,0.000110533,0.0001166233,0.00003658769],"domain_scores_gemma":[0.9996758,0.00007135432,0.00007986772,0.00005497804,0.00007358377,0.00004451714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001406495,0.00001376188,0.002706873,0.00005091064,0.00001260097,0.0001461613,0.00005666313,0.0001712873,0.9929727,0.00006868307,0.00008879744,0.003571026],"study_design_scores_gemma":[0.00002682355,0.000420242,0.03433861,0.00007739387,0.00007450885,0.001367042,0.0003242313,0.007582204,0.9462208,0.0006820973,0.008843523,0.00004238465],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7741804,0.002155381,0.2144738,0.0003505843,0.0001780634,0.0004368963,0.004217475,0.001205901,0.002801658],"genre_scores_gemma":[0.7808942,0.001331502,0.2066514,0.0005878576,0.0001048137,0.0005399038,0.004062472,0.0003397556,0.005488154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001470379,"threshold_uncertainty_score":0.004918933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01415242408831433,"score_gpt":0.2262341622412111,"score_spread":0.2120817381528967,"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."}}