{"id":"W3180844444","doi":"10.22541/au.162542614.43403803/v1","title":"Genotyping-in-thousands by sequencing (GT-seq) of non-invasive fecal and degraded samples: a new panel to enable ongoing monitoring of Canadian polar bear populations","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Nunavut; Government of Northwest Territories; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; Genome Canada; Ontario Genomics; Ontario Genomics Institute; Compute Canada","keywords":"Genotyping; Biology; Population genomics; Population; DNA sequencing; Computational biology; Genomics; Pyrosequencing; Environmental DNA; Genetics; Genotype; Genome; DNA; Ecology; Biodiversity; Gene; Medicine","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":[0.001118813,0.0006581885,0.0006476933,0.0009352883,0.001112165,0.001036529,0.0008067468,0.0006507601,0.001561893],"category_scores_gemma":[0.00152608,0.0003743589,0.000704823,0.00104873,0.0005502782,0.0004280436,0.0009409847,0.001172232,0.0007053686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001571181,"about_ca_system_score_gemma":0.004470202,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1443169,"about_ca_topic_score_gemma":0.4163221,"domain_scores_codex":[0.9984927,0.00009924186,0.00004842334,0.0004587729,0.0007720618,0.0001287702],"domain_scores_gemma":[0.998892,0.0001435045,0.0001965334,0.0001270317,0.0005011521,0.0001398122],"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":[0.0005113936,0.0000971911,0.08475098,0.0004177063,0.0003655198,0.0002667513,0.001245525,0.005734721,0.7978294,0.001504372,0.008950731,0.0983257],"study_design_scores_gemma":[0.0001081412,0.0003687704,0.3909489,0.0001876359,0.0006575193,0.0008746131,0.0009557361,0.03971515,0.4023562,0.002360013,0.1610999,0.0003673978],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6881745,0.002429197,0.2323631,0.001159222,0.0002913073,0.0006203193,0.05660549,0.005115815,0.01324098],"genre_scores_gemma":[0.497475,0.001655568,0.4254927,0.002017845,0.0001121034,0.0006157767,0.05941927,0.001077365,0.01213445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8556831,"threshold_uncertainty_score":0.2869537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0693117838672106,"score_gpt":0.241836386966987,"score_spread":0.1725246030997764,"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."}}