{"id":"W6919194080","doi":"10.60510/awfwi03010","title":"IGSN AWFWI03010 (EN22004-TG07): Individual Sample (Biology, leaf for DNA analyses) of sample EN22004-T07 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001150744,0.001931809,0.001273013,0.006563528,0.002198203,0.001700806,0.002871568,0.001569833,0.1658637],"category_scores_gemma":[0.002756267,0.001079582,0.0007027783,0.01377498,0.000618816,0.0008474988,0.002036473,0.0008773997,0.1694096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002939322,"about_ca_system_score_gemma":0.006827876,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1665916,"about_ca_topic_score_gemma":0.3061016,"domain_scores_codex":[0.9988067,0.00006061697,0.0001080274,0.000390968,0.0003082885,0.0003254216],"domain_scores_gemma":[0.9979413,0.0001618657,0.000197841,0.0006229909,0.0007831948,0.0002928724],"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":[0.0003141561,0.00006858283,0.008166374,0.0009125542,0.00006865082,0.0001453483,0.0005771176,0.0002722689,0.006071796,0.00186195,0.9518153,0.02972605],"study_design_scores_gemma":[0.00007228244,0.00002500445,0.02877119,0.0001660249,0.00006535856,0.0001330098,0.0002169529,0.0001302441,0.002110587,0.000795667,0.9674786,0.00003517789],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001910967,0.00004920395,0.001130147,0.00003534674,0.00003279018,0.00007580779,0.9818026,0.001207338,0.0137557],"genre_scores_gemma":[0.001877994,0.00004603909,0.001907069,0.00005577131,0.000006250962,0.0001731473,0.9879666,0.0009945881,0.006972588],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8341362,"threshold_uncertainty_score":0.5548694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09426691606538702,"score_gpt":0.339044671492214,"score_spread":0.244777755426827,"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."}}