{"id":"W6975283826","doi":"10.60510/awfwi03018","title":"IGSN AWFWI03018 (EN22004-TG17): Individual Sample (Biology, leaf for DNA analyses) of sample EN22004-T17 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":[0.001143627,0.001937414,0.001298661,0.006452876,0.002243536,0.001636006,0.00288293,0.001621993,0.1567205],"category_scores_gemma":[0.002662804,0.001060082,0.0006914361,0.01394836,0.0006573052,0.0008316883,0.00210759,0.0008691223,0.155328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002854316,"about_ca_system_score_gemma":0.006720452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.156126,"about_ca_topic_score_gemma":0.2981162,"domain_scores_codex":[0.9988692,0.00005594348,0.0001045786,0.0003758227,0.0002819718,0.0003124606],"domain_scores_gemma":[0.9981347,0.0001541477,0.0001919413,0.0005450661,0.0006878378,0.000286177],"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":[0.0003833252,0.00007145627,0.009394367,0.001135832,0.00008595541,0.0002047802,0.0007185101,0.0002969343,0.007419826,0.002203011,0.9440618,0.03402431],"study_design_scores_gemma":[0.00007522819,0.00002576497,0.02847856,0.000186441,0.00007583224,0.0001540237,0.0002279544,0.0001145074,0.001973309,0.0008300445,0.967823,0.00003528559],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002192054,0.00006097585,0.001186682,0.00003834485,0.00003632495,0.00007528607,0.9811425,0.001148327,0.01411959],"genre_scores_gemma":[0.002225647,0.00005830291,0.001931611,0.00006128388,0.000006941204,0.0001853705,0.987599,0.001029401,0.0069024],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.843874,"threshold_uncertainty_score":0.5242823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09801057066006315,"score_gpt":0.3408003269351872,"score_spread":0.2427897562751241,"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."}}