{"id":"W4322210520","doi":"10.5194/egusphere-egu23-14704","title":"Manufacturing surprise: How information content, modeling capabilities and decision making purpose influence optimal streamflow monitoring","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Streamflow; Computer science; Predictability; Surprise; Flood forecasting; Resource (disambiguation); Flood myth; Drainage basin; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008060798,0.0009075912,0.001003441,0.001344433,0.0007292576,0.005218796,0.001266773,0.001393145,0.001744524],"category_scores_gemma":[0.08191618,0.0007960825,0.0006525248,0.001074747,0.002000468,0.009694384,0.00288814,0.00206323,0.0002874701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001538709,"about_ca_system_score_gemma":0.001213057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003959072,"about_ca_topic_score_gemma":0.002962388,"domain_scores_codex":[0.9960139,0.001852428,0.0002350543,0.0008852433,0.0006571371,0.0003561556],"domain_scores_gemma":[0.9125926,0.07349126,0.005122115,0.004250799,0.003033173,0.001510065],"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":[0.001298445,0.0002994438,0.1081803,0.0006679344,0.0006241379,0.0009733043,0.001549982,0.5955456,0.005835233,0.1074767,0.006016839,0.171532],"study_design_scores_gemma":[0.00003543316,0.0001664692,0.01606767,0.0001083663,0.0001770236,0.0001906898,0.0003809444,0.7959132,0.003358347,0.1803993,0.003131321,0.00007113728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6313552,0.003921302,0.3369911,0.009792711,0.0002404149,0.0001020478,0.001206621,0.0009663114,0.01542432],"genre_scores_gemma":[0.9777569,0.0005388936,0.02056726,0.0002314241,0.00007844329,0.00002940508,0.0002983948,0.0001069115,0.0003922996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008060798,"threshold_uncertainty_score":0.04263008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03554077195143208,"score_gpt":0.251380717060682,"score_spread":0.21583994510925,"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."}}