{"id":"W771171050","doi":"","title":"An integrated water quality modeling system with dynamic remote sensing feedback","year":2007,"lang":"en","type":"article","venue":"RIT Scholar Works (Rochester Institute of Technology)","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Department of Agriculture; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Water quality; Remote sensing; Quality (philosophy); System dynamics; Computer science; Environmental science; Geology; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.002281246,0.000454548,0.0005620233,0.0004322361,0.0003688934,0.0001289101,0.001207993,0.000886099,0.000007367906],"category_scores_gemma":[0.0001216142,0.0003371813,0.00008907554,0.001007456,0.0009500653,0.001373285,0.000604662,0.001529271,0.00009375669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007512411,"about_ca_system_score_gemma":0.00002077133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006112485,"about_ca_topic_score_gemma":0.0004566657,"domain_scores_codex":[0.9967322,0.00008043449,0.0008765075,0.0008920663,0.0005582062,0.000860569],"domain_scores_gemma":[0.997864,0.00001726454,0.0002312913,0.001663493,0.00009454024,0.0001294277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000547115,0.0002760643,0.01336038,0.0003329055,0.0002078956,0.0003861805,0.001120507,0.03672392,0.5181114,0.0009068245,0.00001272663,0.4280141],"study_design_scores_gemma":[0.00295186,0.000713471,0.006402106,0.003691941,0.0002034571,0.0006523187,0.01265192,0.1056364,0.8554465,0.004248172,0.004651584,0.002750298],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6908198,0.00003957586,0.3067405,0.0003841952,0.0002164833,0.0002696539,0.000002317093,0.001375224,0.0001522413],"genre_scores_gemma":[0.7529567,0.000007073636,0.2469018,0.00002201271,0.00001848243,0.000001753188,0.00001605807,0.00004091408,0.00003523131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4252638,"threshold_uncertainty_score":0.999908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02279118349373165,"score_gpt":0.2727571470848483,"score_spread":0.2499659635911167,"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."}}