{"id":"W6917825017","doi":"10.58052/ieagr00ao","title":"2022-06-06-Yukon-kit-49-archive Grab Liquid>aqueous river water","year":2022,"lang":"en","type":"other","venue":"System for Earth Sample Registration (SESAR)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Hydrology (agriculture); Water resources; Work (physics); Water quality; Shore","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":["insufficient_payload"],"category_scores_codex":[0.0008498548,0.001272491,0.0008051697,0.002469416,0.001370675,0.00229743,0.001739683,0.001511909,0.5488747],"category_scores_gemma":[0.001968691,0.0009133144,0.0006199222,0.001857983,0.0005015253,0.00156113,0.001972278,0.0007186387,0.5141707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002083794,"about_ca_system_score_gemma":0.003696723,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07030605,"about_ca_topic_score_gemma":0.1228043,"domain_scores_codex":[0.9993327,0.00005956291,0.00005203847,0.0001498213,0.0002626623,0.0001431168],"domain_scores_gemma":[0.9988132,0.0001420358,0.00006591345,0.0002652586,0.0005976412,0.0001160459],"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":[0.0002765616,0.00008668056,0.001981118,0.0004207612,0.00002310147,0.00006061963,0.0001316622,0.0004473006,0.004183794,0.00278911,0.9342991,0.05530027],"study_design_scores_gemma":[0.00008286649,0.00002383382,0.002703956,0.00005277346,0.000009126243,0.00003708653,0.0000993578,0.0007418526,0.004909431,0.001210838,0.9900878,0.00004102866],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.004955261,0.0002095701,0.01157689,0.0004902607,0.000249929,0.0007284835,0.6303018,0.04804623,0.3034415],"genre_scores_gemma":[0.02003429,0.0002849177,0.02109678,0.0008480153,0.00005306665,0.001226081,0.5522593,0.02988812,0.3743095],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9296939,"threshold_uncertainty_score":0.6434755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019833860091303,"score_gpt":0.2495448756942345,"score_spread":0.2293465370933215,"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."}}