{"id":"W7133303803","doi":"10.58052/ieagr00ld","title":"2014-06-23-Yukon-kit-13-archive Grab Liquid>aqueous river water","year":2014,"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":[0.001576217,0.001473496,0.001028209,0.004039547,0.001701446,0.002948217,0.00219025,0.001557821,0.3279422],"category_scores_gemma":[0.00311121,0.001331794,0.0008949907,0.003019623,0.0007038825,0.002151947,0.002610265,0.0008647606,0.3461084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002227288,"about_ca_system_score_gemma":0.005497762,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06484538,"about_ca_topic_score_gemma":0.1206975,"domain_scores_codex":[0.998768,0.0001156205,0.000124279,0.0002788073,0.0004925822,0.0002206395],"domain_scores_gemma":[0.9977956,0.0002498528,0.0001568455,0.0005852737,0.001061383,0.0001511121],"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":[0.0006709155,0.0001224251,0.005204993,0.0007795641,0.00006601685,0.0001041953,0.0002110375,0.0007329853,0.009556436,0.00291454,0.896445,0.08319184],"study_design_scores_gemma":[0.0001377062,0.00004065859,0.006898648,0.00009098919,0.00002913508,0.00006118321,0.0001452377,0.001216548,0.01412241,0.001450736,0.9757221,0.00008463697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.007542221,0.0003191909,0.02182565,0.0005204926,0.000281711,0.0009596323,0.7657339,0.06544986,0.1373673],"genre_scores_gemma":[0.02333044,0.0004383024,0.04364521,0.0006303876,0.00007334292,0.001720865,0.721338,0.03287742,0.175946],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9351546,"threshold_uncertainty_score":0.9586089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179530654062216,"score_gpt":0.2451566813859023,"score_spread":0.2272036159796807,"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."}}