{"id":"W7133349480","doi":"10.58052/ieagr00ln","title":"2016-03-03-Yukon-kit-23-archive Grab Liquid>aqueous river water","year":2016,"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":[0.0015981,0.00149594,0.00106899,0.003922982,0.001587378,0.003121415,0.00203347,0.001430094,0.3016398],"category_scores_gemma":[0.003153465,0.00124014,0.0009344252,0.002864699,0.000690297,0.002418897,0.002816238,0.000849733,0.3282782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001860485,"about_ca_system_score_gemma":0.005212848,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05331194,"about_ca_topic_score_gemma":0.09755929,"domain_scores_codex":[0.9988398,0.0001039849,0.0001314338,0.0002855959,0.0004476305,0.0001915578],"domain_scores_gemma":[0.9978756,0.0002454548,0.0001687449,0.0005873252,0.0009788286,0.0001441069],"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":[0.0008312213,0.0001240589,0.006846467,0.001118224,0.00008083945,0.0001417021,0.0002860677,0.0008095204,0.0136913,0.003309343,0.8661633,0.106598],"study_design_scores_gemma":[0.0001412822,0.00004044416,0.007166217,0.0001063266,0.00003504807,0.00007191273,0.0001610141,0.001345155,0.01653131,0.001631525,0.9726825,0.0000871074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008311178,0.0003714069,0.0234074,0.0004846557,0.0002680959,0.000857861,0.7718321,0.0687317,0.1257356],"genre_scores_gemma":[0.0268769,0.0005046404,0.05063305,0.0005683735,0.00008111431,0.001636338,0.7326735,0.03478174,0.1522444],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9466881,"threshold_uncertainty_score":0.9961261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02141446751000286,"score_gpt":0.2601606683016127,"score_spread":0.2387462007916099,"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."}}