{"id":"W2967520929","doi":"10.1101/730440","title":"PalaeoChip Arctic1.0: An optimised eDNA targeted enrichment approach to reconstructing past environments","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University Health Network; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; Garfield Weston Foundation; Arctic Institute of North America; Polar Knowledge Canada; Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Environmental DNA; Ancient DNA; Equus; DNA extraction; Biology; Extraction (chemistry); Computational biology; Evolutionary biology; Ecology; Biodiversity; Chemistry; Genetics; Polymerase chain reaction; Gene; Chromatography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001500716,0.0009198508,0.0006772333,0.00109513,0.0009796931,0.001228862,0.000911204,0.0006625011,0.002076441],"category_scores_gemma":[0.001425183,0.0009607042,0.0006550046,0.0006211255,0.0004912337,0.000411991,0.001605236,0.001072505,0.001708109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005294212,"about_ca_system_score_gemma":0.001238909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004196404,"about_ca_topic_score_gemma":0.01761013,"domain_scores_codex":[0.9993165,0.00007230719,0.00005143087,0.0002673508,0.0002091913,0.00008322626],"domain_scores_gemma":[0.9995344,0.000142693,0.00006686919,0.00006285097,0.0001387841,0.00005443897],"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":[0.0004987763,0.00004687856,0.00643102,0.0005355247,0.0001451305,0.0003008931,0.0005298713,0.007278039,0.9487331,0.0009508784,0.003168688,0.0313812],"study_design_scores_gemma":[0.00009541169,0.000318672,0.03183604,0.0001757583,0.0002397509,0.001249605,0.0005511433,0.07709356,0.79084,0.002301635,0.09509866,0.000199716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.38153,0.001541635,0.5601516,0.0005515813,0.0002348961,0.0007058618,0.03025243,0.01682385,0.008208268],"genre_scores_gemma":[0.1536425,0.0005289358,0.8071295,0.0003182763,0.00003941719,0.0007528494,0.02943273,0.003034179,0.005121683],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004196404,"threshold_uncertainty_score":0.008343935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0150916295616172,"score_gpt":0.195291350848139,"score_spread":0.1801997212865218,"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."}}