{"id":"W2919419084","doi":"10.5194/hess-23-1145-2019","title":"Incorporating the logistic regression into a decision-centric assessment of climate change impacts on a complex river system","year":2019,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alberta Environment and Parks","keywords":"Climate change; Robustness (evolution); Environmental science; Logistic regression; Environmental resource management; Computer science; Water supply; Drainage basin; Decision support system; Structural basin; Water resources; Water resource management; Risk analysis (engineering); Business; Data mining; Environmental engineering; Geology; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004198,0.0009856771,0.0007893525,0.001425341,0.0004962157,0.001861089,0.0009658488,0.0008199067,0.001475014],"category_scores_gemma":[0.01024979,0.0004255433,0.001041644,0.001022592,0.0009428216,0.001621099,0.001818713,0.001237534,0.0001132542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139947,"about_ca_system_score_gemma":0.001583714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007637944,"about_ca_topic_score_gemma":0.005698213,"domain_scores_codex":[0.9976879,0.001476655,0.00009447981,0.0003123728,0.0002630292,0.000165473],"domain_scores_gemma":[0.9933959,0.00465623,0.000715844,0.000199891,0.0008287054,0.0002034851],"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":[0.00003350885,0.00003228196,0.005606828,0.00003415278,0.00006637791,0.00006706159,0.00003435817,0.9829228,0.0003549162,0.003088963,0.00009966871,0.007659062],"study_design_scores_gemma":[0.000001444164,0.00002110862,0.0006124586,0.000002874818,0.000007713886,0.000006200481,0.00002175143,0.9975408,0.00009742021,0.001612553,0.00007096203,0.00000473469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.328711,0.0003132544,0.6661372,0.0008289502,0.00004690593,0.000145082,0.0001776697,0.0002158043,0.003424176],"genre_scores_gemma":[0.9786203,0.00008579935,0.02066834,0.00003705634,0.00001806104,0.00004536247,0.00006163092,0.00001269129,0.0004506687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007637944,"threshold_uncertainty_score":0.02220142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03770398483987475,"score_gpt":0.2888835940379896,"score_spread":0.2511796091981149,"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."}}