{"id":"W6937618451","doi":"10.60510/awfwi02985","title":"IGSN AWFWI02985 (EN22003-TG03): Individual Sample (Biology, leaf for DNA analyses) of sample EN22003-T03 from Squanga Lake (Big Salmon Range), Yukon, CA","year":2024,"lang":"en","type":"other","venue":"GFZ IGSN Sample Catalogue","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Sample (material); DNA; Fish <Actinopterygii>; Aquatic animal","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001087125,0.001797136,0.001155507,0.005878844,0.002076945,0.001600281,0.002811605,0.001509207,0.1597498],"category_scores_gemma":[0.002476953,0.001006373,0.000687591,0.01239321,0.0005774098,0.0008103001,0.001941566,0.000842956,0.1603254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002629907,"about_ca_system_score_gemma":0.005850757,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1597201,"about_ca_topic_score_gemma":0.3131503,"domain_scores_codex":[0.9989603,0.00005176372,0.000092128,0.00034848,0.0002700883,0.0002772456],"domain_scores_gemma":[0.9982721,0.0001302186,0.0001657339,0.0005158106,0.0006576115,0.000258462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003160968,0.00006391798,0.008048255,0.0009828528,0.00007023331,0.0001775387,0.0006603714,0.0002858972,0.006876029,0.002137824,0.9463382,0.0340427],"study_design_scores_gemma":[0.00006187378,0.00002168588,0.02770816,0.0001724244,0.00006374198,0.000137015,0.0001961722,0.0001201433,0.001957757,0.0008710647,0.968657,0.00003294903],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002162084,0.00005736332,0.001432019,0.00003954411,0.0000391615,0.0000814795,0.9781254,0.001296831,0.01676606],"genre_scores_gemma":[0.002290275,0.00005500645,0.002275222,0.00007007055,0.000006970793,0.0001883447,0.9851386,0.001162082,0.008813434],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8402799,"threshold_uncertainty_score":0.5344163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09582849170348648,"score_gpt":0.3381577183161986,"score_spread":0.2423292266127121,"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."}}