{"id":"W6973836088","doi":"10.58052/ieagr003u","title":"2018-09-27-Yukon-kit-39-archive Grab Liquid>aqueous river water","year":2018,"lang":"en","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":[0.001240674,0.001488231,0.0009042934,0.003017538,0.001485276,0.002850411,0.001988592,0.001593531,0.4784515],"category_scores_gemma":[0.002741723,0.001096028,0.000820532,0.002162537,0.0006746684,0.002422931,0.002539608,0.0009021264,0.4898206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002299275,"about_ca_system_score_gemma":0.004579884,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06128624,"about_ca_topic_score_gemma":0.09945452,"domain_scores_codex":[0.9990127,0.00009343559,0.0000805148,0.0002131521,0.0004120964,0.0001881678],"domain_scores_gemma":[0.9982161,0.0001988268,0.00009854983,0.000491117,0.0008565992,0.0001386958],"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":[0.0004092589,0.00009329715,0.002410092,0.0004640391,0.00003164975,0.00009596669,0.0001741739,0.0006163423,0.00437131,0.002968026,0.9201082,0.06825758],"study_design_scores_gemma":[0.00008087294,0.00002301272,0.00292309,0.00007367034,0.00001079913,0.00004495199,0.00009861923,0.0008212116,0.006315373,0.001348308,0.9882149,0.00004531454],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.006319946,0.00032301,0.0202984,0.0006750948,0.0004398044,0.0008152794,0.5945268,0.07853484,0.2980669],"genre_scores_gemma":[0.02361254,0.0004493716,0.03032776,0.0006824509,0.00009481116,0.001309201,0.5458866,0.04799194,0.3496453],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9387137,"threshold_uncertainty_score":0.7439257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02501810159687771,"score_gpt":0.2574105633481427,"score_spread":0.232392461751265,"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."}}