{"id":"W6910268552","doi":"10.48511/8nzy-7a51","title":"Wolverton Creek Hydrometric","year":2023,"lang":"en","type":"dataset","venue":"Columbia Basin Water Hub","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural basin; Water supply; Foundation (evidence); Hydrology (agriculture); Habitat; Water resources; Watershed","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001346624,0.0008224591,0.00146195,0.001835944,0.0004845638,0.002035904,0.00235952,0.001174683,0.009167425],"category_scores_gemma":[0.0005613762,0.00105818,0.0005320836,0.003663514,0.0003023964,0.0003246495,0.001437045,0.001455811,0.5661705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007827422,"about_ca_system_score_gemma":0.0002015544,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1880329,"about_ca_topic_score_gemma":0.1648973,"domain_scores_codex":[0.9929093,0.0004301536,0.001076857,0.00186521,0.00169361,0.002024898],"domain_scores_gemma":[0.9953809,0.0002642046,0.0003407891,0.003238812,0.0002561595,0.0005191845],"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":[0.00002808952,0.0002541945,0.0002053533,0.0003355822,0.0002709329,0.0008799223,0.00002236179,0.00000384366,0.0001281265,5.767166e-8,0.9976363,0.0002352904],"study_design_scores_gemma":[0.0009371394,0.0001914361,0.001806061,0.0002459592,0.0004437718,0.0000610788,0.00001159958,0.00001258652,0.0001443786,0.0001956864,0.9944921,0.001458166],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005304019,0.0001132793,6.37392e-7,0.00007232695,0.003453443,0.00113247,0.9884277,0.001140679,0.0003554717],"genre_scores_gemma":[0.0001252119,0.0001144735,0.00004736026,0.000286348,0.0007709784,0.0003078248,0.9507791,0.0006508858,0.04691777],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.557003,"threshold_uncertainty_score":0.9991869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02343487422902443,"score_gpt":0.250421696101443,"score_spread":0.2269868218724186,"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."}}