{"id":"W6975877403","doi":"10.60510/awfwi03000","title":"IGSN AWFWI03000 (EN22003-TG18): Individual Sample (Biology, leaf for DNA analyses) of sample EN22003-T18 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":[0.001100301,0.001804073,0.001207593,0.006011175,0.002145957,0.001499032,0.00276769,0.001404634,0.1512857],"category_scores_gemma":[0.002333797,0.0009874617,0.000629641,0.01302221,0.00060047,0.0007884635,0.001876807,0.0008362273,0.1534099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002785804,"about_ca_system_score_gemma":0.006180017,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1518854,"about_ca_topic_score_gemma":0.3019219,"domain_scores_codex":[0.9989453,0.00005180947,0.00009401526,0.0003607464,0.0002661385,0.0002820368],"domain_scores_gemma":[0.9982538,0.0001289431,0.0001676104,0.0005337295,0.0006535859,0.0002624383],"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":[0.0003913254,0.00007893144,0.009926952,0.001079949,0.00007631956,0.0001875572,0.0007669835,0.0003000499,0.009458581,0.002426121,0.9343658,0.04094148],"study_design_scores_gemma":[0.00006482865,0.00002415722,0.02826517,0.0001529266,0.00006354027,0.0001391788,0.0002050057,0.0001181964,0.002314889,0.0007513156,0.9678688,0.00003191728],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002640903,0.00006179018,0.001583678,0.00003916368,0.0000350082,0.00009326399,0.9774516,0.001383619,0.01671104],"genre_scores_gemma":[0.002493147,0.00005916333,0.00256483,0.00006209904,0.000006730775,0.000216414,0.9849237,0.001160632,0.008513242],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8481146,"threshold_uncertainty_score":0.5061011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09545915219740336,"score_gpt":0.3381945466980362,"score_spread":0.2427353945006329,"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."}}