{"id":"W6891708955","doi":"10.48511/5zsc-cg07","title":"Dunbar 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; Tributary; Hydrology (agriculture); Foundation (evidence); Water resources; Climate change; Drainage basin","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":[0.001484477,0.0008283384,0.001497091,0.001833971,0.0005129442,0.002075008,0.002453441,0.001143047,0.008827188],"category_scores_gemma":[0.000659325,0.001073077,0.0005303006,0.003665283,0.000330541,0.0003011297,0.001496925,0.001460868,0.6692085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006400546,"about_ca_system_score_gemma":0.000205654,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1618567,"about_ca_topic_score_gemma":0.1504403,"domain_scores_codex":[0.9927542,0.0004971572,0.00111563,0.001882747,0.001685782,0.00206451],"domain_scores_gemma":[0.9951921,0.0002575818,0.0003464698,0.003380763,0.0002734818,0.0005495874],"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":[0.00002089058,0.0002392528,0.0001733043,0.0003718032,0.0002803686,0.001133352,0.00002299071,0.000004302579,0.0001102453,9.553168e-8,0.9975065,0.0001368859],"study_design_scores_gemma":[0.0009162286,0.0001821735,0.002081526,0.0001988867,0.0004757482,0.00007983798,0.00001324506,0.00000971929,0.000101123,0.0002224502,0.9942473,0.001471785],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004432247,0.0001147389,6.047179e-7,0.00008270387,0.003315461,0.001155595,0.9894049,0.001128627,0.0003651276],"genre_scores_gemma":[0.0001292453,0.0001008864,0.00005824797,0.0002919176,0.0009616885,0.0003326696,0.957648,0.0007061473,0.03977115],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6603813,"threshold_uncertainty_score":0.999172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317085213427272,"score_gpt":0.2500708765099384,"score_spread":0.2269000243756656,"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."}}