{"id":"W2998415386","doi":"10.1101/2020.01.02.893438","title":"Optimization of subsampling, decontamination, and DNA extraction of difficult peat and silt permafrost samples","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Polar Knowledge Canada","keywords":"Human decontamination; Permafrost; TRACER; Extraction (chemistry); Peat; Contamination; Environmental science; Coring; Environmental chemistry; Waste management; Ecology; Chromatography; Chemistry; Materials science; Biology; Drilling","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":[0.002536502,0.001048986,0.000730832,0.001147309,0.0008277292,0.0009906933,0.0008379176,0.0005988768,0.001447987],"category_scores_gemma":[0.003735433,0.0005150337,0.000561082,0.0006245706,0.0007725003,0.0004685844,0.0007413688,0.0006746902,0.001250933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002153938,"about_ca_system_score_gemma":0.0008165458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001376836,"about_ca_topic_score_gemma":0.006190781,"domain_scores_codex":[0.9976526,0.000519996,0.0002812442,0.0006183801,0.000739618,0.0001881315],"domain_scores_gemma":[0.9981968,0.0005568885,0.000308871,0.0001854873,0.0006481265,0.0001038449],"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":[0.0001391606,0.0001262511,0.005409215,0.0003270575,0.00002693545,0.0001334771,0.0005327628,0.0004713038,0.969652,0.000135226,0.0003081832,0.02273836],"study_design_scores_gemma":[0.00002381809,0.001082828,0.03352908,0.0001645008,0.0001175853,0.0005808615,0.0006777326,0.004665842,0.9400854,0.0003957593,0.01859867,0.00007800628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6198755,0.001980477,0.3685322,0.000420083,0.0002868454,0.002797898,0.001503924,0.001403814,0.003199291],"genre_scores_gemma":[0.3069046,0.001708116,0.6811365,0.0004313904,0.0001149101,0.002420597,0.003973712,0.0007040449,0.002606101],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002536502,"threshold_uncertainty_score":0.0134145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03047581992714962,"score_gpt":0.2280878970762993,"score_spread":0.1976120771491497,"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."}}