{"id":"W2168016241","doi":"10.1007/978-3-642-19536-5_9","title":"A Model-Driven Approach to Uncertainty Reduction in Environmental Data","year":2011,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Raw data; Computer science; Sampling (signal processing); Data mining; Environmental data; Sampling design; Aggregate (composite); Data quality; Simple random sample; Data set; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004458953,0.0003651447,0.0002635352,0.0001775574,0.0002049722,0.00002582832,0.0007656845,0.0001392958,0.0002881173],"category_scores_gemma":[0.000006659366,0.0003620387,0.0000283719,0.00006984844,0.0007864216,0.0005377213,0.002588792,0.0002973858,0.0003063837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006116045,"about_ca_system_score_gemma":0.000006548304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000536053,"about_ca_topic_score_gemma":0.000009534754,"domain_scores_codex":[0.9975872,0.000005820924,0.0002537204,0.001138092,0.000516461,0.0004987275],"domain_scores_gemma":[0.999118,0.000006693329,0.00005841693,0.000616289,5.057991e-7,0.0002001067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006335532,0.0002918102,0.002592677,0.00004716442,0.00008116732,0.00003598341,0.004659548,0.9453369,0.02370345,0.002455583,0.002607774,0.01812462],"study_design_scores_gemma":[0.001050913,0.0003072104,0.02120899,0.0001465843,0.0001928982,0.00009568974,0.0004062013,0.8487919,0.0002778,0.002237607,0.122098,0.003186168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2741596,0.0004576945,0.002945071,0.0003275274,0.0005754994,0.002475487,0.000352882,0.0002006442,0.7185056],"genre_scores_gemma":[0.9762763,0.0005279933,0.002760026,0.0001172496,0.00004283064,0.00003439654,0.000092457,0.00004163621,0.02010706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7021168,"threshold_uncertainty_score":0.9998832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245210618698968,"score_gpt":0.1866678950883288,"score_spread":0.162146833218432,"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."}}