{"id":"W4416542866","doi":"10.1016/j.ecolind.2025.114458","title":"Influence of drought identification methods on analyzing and assessing responses of water quality to droughts","year":2025,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Streamflow; Water quality; Identification (biology); Hydrology (agriculture); Water resources; Nutrient; Water use","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01904978,0.0004589455,0.000431534,0.0008615022,0.0005763551,0.001272689,0.000438008,0.0003804784,0.0004193728],"category_scores_gemma":[0.0356078,0.0001738121,0.0004561569,0.0008685275,0.0004656945,0.0006357384,0.001126262,0.0004578981,0.0001225459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005988768,"about_ca_system_score_gemma":0.0008326251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00767728,"about_ca_topic_score_gemma":0.01654793,"domain_scores_codex":[0.9917629,0.004315749,0.0009733109,0.001536276,0.001213693,0.0001980744],"domain_scores_gemma":[0.9764981,0.01657282,0.002584342,0.001415903,0.002592293,0.000336515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001735655,0.0002445903,0.7015541,0.0007666977,0.001117582,0.0001464701,0.00239864,0.009901062,0.04477633,0.001062071,0.001291551,0.2350054],"study_design_scores_gemma":[0.0001269292,0.0008515359,0.8799754,0.0002414394,0.0005788315,0.0003483225,0.00176499,0.07516742,0.02929274,0.002031633,0.00946841,0.0001523204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9087442,0.00194769,0.08476131,0.0002851344,0.0001378537,0.0002851898,0.0009576014,0.0002343021,0.002646776],"genre_scores_gemma":[0.9453154,0.000610006,0.05198463,0.0001456239,0.00004597342,0.0002373424,0.001057871,0.00009122395,0.0005119006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01904978,"threshold_uncertainty_score":0.100746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02423528045080009,"score_gpt":0.3720582996554211,"score_spread":0.3478230192046211,"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."}}