{"id":"W2101443105","doi":"10.1007/bf03326131","title":"Forecast of water level and ice jam thickness using the back propagation neural network and support vector machine methods","year":2010,"lang":"en","type":"article","venue":"International Journal of Environmental Science and Technology","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Northern British Columbia","funders":"Natural Science Foundation of Anhui Province; Hefei University of Technology; Hefei University; National Natural Science Foundation of China","keywords":"Artificial neural network; Ice formation; Flooding (psychology); Environmental science; Water level; Meteorology; Geology; Hydrology (agriculture); Computer science; Atmospheric sciences; Geotechnical engineering; Artificial intelligence; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0007693248,0.0000598277,0.00008591786,0.00008391347,0.0001406959,0.00003030717,0.0002293013,0.00004436052,0.0001719005],"category_scores_gemma":[0.00003685346,0.00003265614,0.00001200849,0.00007140046,0.001362166,0.0002878871,0.00008012208,0.000200104,7.906871e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005765753,"about_ca_system_score_gemma":0.00001999096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005064439,"about_ca_topic_score_gemma":0.00004877276,"domain_scores_codex":[0.999343,0.00001890269,0.0001737441,0.000100471,0.0002391001,0.0001247676],"domain_scores_gemma":[0.9996947,0.00005235675,0.0001304814,0.00004860012,0.00003338969,0.00004044769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004809027,0.00002015254,0.6122586,0.000004596793,0.00002595008,0.0000120966,0.0003299435,0.0002065869,0.1196004,0.0004221545,0.000003684221,0.2670678],"study_design_scores_gemma":[0.0006714293,0.0005011295,0.8509973,0.00002659474,0.0000519262,0.006934081,0.001130884,0.1206752,0.01171676,0.005835023,0.001261961,0.0001977657],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966403,0.000055518,0.001176374,0.001687402,0.0003195345,0.00004670127,0.00001279396,0.000001591766,0.00005982163],"genre_scores_gemma":[0.9885123,0.0000806893,0.01123799,0.00009732951,0.00005507148,1.016583e-7,0.000002210823,0.000001315819,0.00001295271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.26687,"threshold_uncertainty_score":0.5018957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490481561455598,"score_gpt":0.255366657430974,"score_spread":0.240461841816418,"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."}}