{"id":"W7117144464","doi":"10.1016/j.jhazmat.2025.140879","title":"Towards precision limnology: An explainable AI framework decoding spatiotemporal algal dynamics in Chinese major lakes","year":2025,"lang":"en","type":"article","venue":"Journal of Hazardous Materials","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; York University","funders":"Key Scientific Research Project of Colleges and Universities in Henan Province; National Key Research and Development Program of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Algal bloom; Eutrophication; Threatened species; Resource (disambiguation); Water quality; Ecosystem; Climate change; Key (lock)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006407871,0.0004689571,0.0004765432,0.0006961093,0.0007515355,0.001408183,0.001072878,0.0006401681,0.001213048],"category_scores_gemma":[0.002961666,0.0003270329,0.000652632,0.0007642232,0.0009617886,0.001838588,0.001289285,0.0008042745,0.0001070856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258843,"about_ca_system_score_gemma":0.002174075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02849924,"about_ca_topic_score_gemma":0.01951267,"domain_scores_codex":[0.999836,0.000040427,0.0000119128,0.00005463642,0.00002435298,0.00003267951],"domain_scores_gemma":[0.9993458,0.0003380841,0.0001014462,0.00007980938,0.0001062317,0.00002855023],"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":[0.00004216162,0.00002305127,0.01256176,0.0000836437,0.00009586669,0.0001200475,0.0003193814,0.8581971,0.001995882,0.09404197,0.001030748,0.03148833],"study_design_scores_gemma":[0.000002308104,0.000003965927,0.0007615465,0.000003178516,0.00001057702,0.000005255672,0.00002287716,0.9743396,0.0001247266,0.02451841,0.00020348,0.000004190851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2699535,0.0007371985,0.7199308,0.001572639,0.00007556373,0.00005666916,0.0007440305,0.0005116783,0.006417869],"genre_scores_gemma":[0.9660671,0.0002862651,0.03191535,0.00009206643,0.0000500231,0.0000654633,0.0003219465,0.00005138538,0.001150399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02849924,"threshold_uncertainty_score":0.05666673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005662303765361311,"score_gpt":0.2715105552162078,"score_spread":0.2658482514508465,"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."}}