{"id":"W7133344533","doi":"10.58052/ieagr00lg","title":"2015-01-26-Yukon-kit-16-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":[0.001739608,0.001459638,0.001018767,0.003712713,0.001552192,0.002598721,0.002112054,0.001489652,0.3109719],"category_scores_gemma":[0.00316338,0.001242497,0.0008922206,0.002562417,0.0006568494,0.002124844,0.002813049,0.0008111646,0.3358807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001871192,"about_ca_system_score_gemma":0.005069975,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04413881,"about_ca_topic_score_gemma":0.0837203,"domain_scores_codex":[0.998835,0.0001183638,0.0001297288,0.0002844661,0.0004387348,0.0001935978],"domain_scores_gemma":[0.9979272,0.0002593907,0.000174599,0.0005669861,0.000917604,0.0001542483],"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":[0.0007450451,0.0001287869,0.005885345,0.00099298,0.00007152613,0.0001178259,0.0002209489,0.0007651361,0.01196688,0.003271997,0.885335,0.09049858],"study_design_scores_gemma":[0.0001518094,0.0000478069,0.006709831,0.0001029182,0.00003218265,0.00006689873,0.0001312074,0.001311232,0.01508713,0.001607804,0.9746659,0.00008542552],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007886956,0.0003664604,0.02496306,0.0005194409,0.0002900021,0.001061025,0.7664072,0.075504,0.1230019],"genre_scores_gemma":[0.02497044,0.0004825723,0.0519691,0.0007540209,0.00008508975,0.002010928,0.7434803,0.0323149,0.1439327],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9558612,"threshold_uncertainty_score":0.9828149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03078542606378275,"score_gpt":0.2731590416673063,"score_spread":0.2423736156035235,"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."}}