{"id":"W7133299877","doi":"10.58052/ieagr00lj","title":"2015-06-11-Yukon-kit-19-archive Grab Liquid>aqueous river water","year":2015,"lang":"","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":[0.001591081,0.001428588,0.0009992465,0.003679381,0.001598609,0.002828884,0.002104004,0.001454214,0.3243079],"category_scores_gemma":[0.003075767,0.00122486,0.0008836498,0.002705683,0.0006579467,0.002193148,0.002778353,0.0008088752,0.339774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002043244,"about_ca_system_score_gemma":0.005250464,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05796521,"about_ca_topic_score_gemma":0.1067508,"domain_scores_codex":[0.9988577,0.0001126609,0.0001228658,0.0002690593,0.0004406103,0.0001971302],"domain_scores_gemma":[0.9978916,0.0002490852,0.0001656344,0.0005751418,0.0009704885,0.0001481944],"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":[0.0006732872,0.0001161836,0.005561362,0.0008885853,0.00006594179,0.0001129931,0.0002195493,0.0007351254,0.009462292,0.003223152,0.8940212,0.08492033],"study_design_scores_gemma":[0.0001341526,0.00004090307,0.006959141,0.00009836609,0.00002825588,0.00006075036,0.000140945,0.001280989,0.01308258,0.001582018,0.9765127,0.00007919419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.007398464,0.0003291749,0.02077927,0.0004999764,0.0002656761,0.0009356771,0.7735422,0.0635426,0.1327069],"genre_scores_gemma":[0.02486895,0.0004521872,0.04378351,0.000667308,0.00007613853,0.001683197,0.7465614,0.03009498,0.1518123],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9420348,"threshold_uncertainty_score":0.9637927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319288799344391,"score_gpt":0.2791065395148421,"score_spread":0.2459136515213982,"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."}}