{"id":"W6955015185","doi":"10.58052/ieagr003w","title":"2019-04-09-Yukon-kit-41-archive Grab Liquid>aqueous river water","year":2019,"lang":"en","type":"other","venue":"System for Earth Sample Registration (SESAR)","topic":"Chemical Reactions and Mechanisms","field":"Chemistry","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":[0.0009429232,0.001186118,0.0007763466,0.002390186,0.001285074,0.002314417,0.001578805,0.001285131,0.489609],"category_scores_gemma":[0.001659092,0.0007899865,0.0006194412,0.001739573,0.0005099655,0.001857038,0.002091118,0.0007868097,0.4851824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002431085,"about_ca_system_score_gemma":0.003996511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05048233,"about_ca_topic_score_gemma":0.08902946,"domain_scores_codex":[0.9993774,0.00005557817,0.00005638469,0.0001548353,0.0002406253,0.0001151249],"domain_scores_gemma":[0.9988804,0.0001183499,0.00007537715,0.0002272341,0.000579747,0.0001188014],"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":[0.0005647446,0.0001303827,0.002672866,0.000758109,0.00003509123,0.000106161,0.0001354853,0.0006955698,0.01184612,0.005112819,0.8234217,0.154521],"study_design_scores_gemma":[0.00007452677,0.0000326494,0.002735669,0.0000583851,0.00001119292,0.00005495422,0.00007505916,0.0007675063,0.009598565,0.001376832,0.9851741,0.00004057623],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.008950748,0.000580584,0.02139551,0.000998251,0.0005107724,0.0009520152,0.503827,0.07844803,0.384337],"genre_scores_gemma":[0.02800014,0.0006783489,0.03112368,0.0009462318,0.00007604102,0.001166497,0.3861318,0.0264458,0.5254315],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.510391,"threshold_uncertainty_score":0.7280108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421934260330731,"score_gpt":0.2360100099685441,"score_spread":0.2217906673652368,"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."}}