{"id":"W2128075723","doi":"10.1111/j.1752-1688.2001.tb05476.x","title":"MODELING TRANSIENT pH DEPRESSIONS IN COASTAL STREAMS OF BRITISH COLUMBIA USING NEURAL NETWORKS<sup>1</sup>","year":2001,"lang":"en","type":"article","venue":"JAWRA Journal of the American Water Resources Association","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Watershed; Environmental science; Hydrology (agriculture); Precipitation; Urbanization; Watershed area; Dry season; Geography; Ecology; Meteorology; Geology; Machine learning; Computer science; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007959532,0.00009609014,0.0003320975,0.00003598231,0.0001531447,0.00009071969,0.0003392946,0.00005893116,0.00007945939],"category_scores_gemma":[0.0001159814,0.00008292958,0.0001775717,0.0003908993,0.0001431013,0.0001764591,0.0001452799,0.0003676449,0.000001169958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520762,"about_ca_system_score_gemma":0.000005665214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03586253,"about_ca_topic_score_gemma":0.006198982,"domain_scores_codex":[0.9979101,0.0003796875,0.0005898365,0.0001598431,0.0005828626,0.0003776632],"domain_scores_gemma":[0.9990286,0.00007177384,0.0006573854,0.0001262817,0.00003413356,0.00008179355],"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":[0.00002738024,0.00005607542,0.3305192,8.430873e-7,0.00001255459,0.000009960207,0.0007086808,0.6635783,0.0009736937,5.264595e-9,0.00005526368,0.004058046],"study_design_scores_gemma":[0.0003603986,0.0001063834,0.06042355,0.00007417546,0.00003966921,0.00008814444,0.0002058742,0.9384043,0.00002502472,0.00003716429,0.0001374576,0.00009785692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988572,0.00001674564,0.0006463701,0.0002499617,0.00006024892,0.00009060233,0.000007205826,0.000008498597,0.00006319489],"genre_scores_gemma":[0.9992749,0.00001657693,0.0003586781,0.0001468435,0.00008127055,8.429688e-7,0.000001239919,0.00001539746,0.0001042228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.274826,"threshold_uncertainty_score":0.9705577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01187992541045528,"score_gpt":0.2174646000421632,"score_spread":0.2055846746317079,"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."}}