{"id":"W6975335374","doi":"10.60510/awfwi03008","title":"IGSN AWFWI03008 (EN22004-TG05): Individual Sample (Biology, leaf for DNA analyses) of sample EN22004-T05 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":[0.001190546,0.001896183,0.001251565,0.006527053,0.002217165,0.001668822,0.002857018,0.001593236,0.1604778],"category_scores_gemma":[0.002784035,0.001051533,0.0006936428,0.01372281,0.0006217101,0.0008388251,0.002023075,0.0008714383,0.1578179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002964384,"about_ca_system_score_gemma":0.006873614,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.171809,"about_ca_topic_score_gemma":0.3171937,"domain_scores_codex":[0.9988008,0.00006260869,0.0001086581,0.0003944838,0.0003081493,0.000325348],"domain_scores_gemma":[0.9980223,0.0001573949,0.000194219,0.0005807657,0.0007623362,0.0002829403],"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":[0.0003435268,0.00007151678,0.008493007,0.001006974,0.00007164656,0.0001636931,0.000648523,0.0002893184,0.006430977,0.002054041,0.9486778,0.03174875],"study_design_scores_gemma":[0.00006923742,0.00002534901,0.02925297,0.0001739814,0.00006530962,0.000133229,0.0002173461,0.0001242407,0.002008435,0.0007989344,0.9670972,0.00003382917],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00203635,0.00005467583,0.001219408,0.00003760751,0.00003630498,0.00008091445,0.9809595,0.001127725,0.0144474],"genre_scores_gemma":[0.002087095,0.00005060085,0.002051405,0.00006085824,0.000006745178,0.0001874416,0.987003,0.0009490918,0.007603684],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.828191,"threshold_uncertainty_score":0.5368516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09662078339932599,"score_gpt":0.3379006059179359,"score_spread":0.2412798225186099,"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."}}