{"id":"W7133342451","doi":"10.58052/ieagr00l9","title":"2013-09-11-Yukon-kit-9-archive Grab Liquid>aqueous river water","year":2013,"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":[0.00151248,0.001509968,0.001034122,0.004038258,0.001609473,0.002868845,0.002209898,0.001493931,0.3209811],"category_scores_gemma":[0.003009077,0.001300931,0.0008786337,0.002993199,0.0006523911,0.002224323,0.002726494,0.0008320152,0.3367712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00211778,"about_ca_system_score_gemma":0.00511577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06224414,"about_ca_topic_score_gemma":0.1178619,"domain_scores_codex":[0.9988129,0.0001078171,0.0001277749,0.0002596303,0.0004861473,0.0002057746],"domain_scores_gemma":[0.997907,0.0002434399,0.000157484,0.0005418676,0.001006975,0.0001431682],"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":[0.0006598656,0.0001241179,0.005232126,0.0008112192,0.00006284415,0.0001241565,0.0002135516,0.0007039449,0.009942682,0.003161636,0.8854472,0.09351674],"study_design_scores_gemma":[0.0001357933,0.00003799303,0.006263125,0.00008897927,0.00002823407,0.00007088723,0.0001430419,0.001282077,0.01439682,0.001539132,0.975934,0.00007988939],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.007674605,0.0003482935,0.02532551,0.0005653691,0.0002678008,0.001014069,0.741487,0.07236628,0.1509511],"genre_scores_gemma":[0.023789,0.000484616,0.04961512,0.0006660136,0.00007578552,0.001746611,0.7027664,0.03353937,0.1873171],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6790189,"threshold_uncertainty_score":0.968538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02174527975944105,"score_gpt":0.2449928277463692,"score_spread":0.2232475479869281,"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."}}