{"id":"W6898997432","doi":"10.58052/ieagr00b4","title":"2023-08-11-Yukon-kit-68-archive Grab Liquid>aqueous river water","year":2023,"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":[0.0008713416,0.001186165,0.0007270302,0.002317911,0.001265679,0.002047258,0.001732526,0.001504535,0.5352216],"category_scores_gemma":[0.001923865,0.0008760326,0.0006237826,0.001675532,0.0004632841,0.001432286,0.00195104,0.0006964108,0.5116786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936691,"about_ca_system_score_gemma":0.003518017,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06219086,"about_ca_topic_score_gemma":0.1145191,"domain_scores_codex":[0.9993464,0.00005856662,0.0000516819,0.0001355763,0.0002676114,0.0001401406],"domain_scores_gemma":[0.9988398,0.0001395062,0.00006868336,0.0002564223,0.0005796019,0.0001159103],"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":[0.000271932,0.00008757951,0.002170641,0.0003932103,0.00002326629,0.00006781167,0.0001220934,0.0004566586,0.004468063,0.002723077,0.9284188,0.06079681],"study_design_scores_gemma":[0.00008280991,0.00002475058,0.002708378,0.00005281468,0.000009126668,0.00003889775,0.00008875866,0.0007867887,0.005185899,0.001206371,0.9897752,0.00004027366],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.005437221,0.0002037892,0.01460766,0.0005624921,0.0002704019,0.0008757385,0.5763431,0.05709747,0.3446022],"genre_scores_gemma":[0.02095382,0.0002845123,0.02413775,0.001033852,0.00006309949,0.001366627,0.5191008,0.02983428,0.4032253],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9378091,"threshold_uncertainty_score":0.6629499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02659163736487617,"score_gpt":0.2616783220464644,"score_spread":0.2350866846815882,"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."}}