{"id":"W4408370430","doi":"10.1002/esp.70027","title":"The unrepeatable river: Exploring chaotic variability in laboratory channels","year":2025,"lang":"en","type":"article","venue":"Earth Surface Processes and Landforms","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BGC Engineering (Canada); University of British Columbia","funders":"Mitacs","keywords":"Geology; Chaotic; Hydrology (agriculture); Geomorphology; Computer science; Geotechnical engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001033522,0.0001184976,0.000173051,0.00004334161,0.0003584349,0.0003194296,0.0003262578,0.00003376989,0.000005124813],"category_scores_gemma":[0.0002022993,0.00007126919,0.000023645,0.0010217,0.00006329413,0.000747743,0.0002214876,0.0001270171,0.000003895672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001116983,"about_ca_system_score_gemma":0.000114841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001321158,"about_ca_topic_score_gemma":0.0002381108,"domain_scores_codex":[0.9990104,0.00003810859,0.000217918,0.0003038122,0.0001257788,0.0003039659],"domain_scores_gemma":[0.9992481,0.0002362431,0.00006008544,0.0002927316,0.0001153692,0.00004742639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001357577,0.000300893,0.5990598,0.00190574,0.0002480315,0.00003672456,0.01326743,0.02178922,0.000201843,0.1001027,0.0002676187,0.2626843],"study_design_scores_gemma":[0.002849141,0.0003543863,0.1325314,0.001092142,0.00007750536,0.00001032303,0.004303676,0.6247857,0.01430338,0.05039801,0.167528,0.001766349],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9459475,0.003497157,0.04729374,0.001012674,0.0003345288,0.0002077883,0.000004354852,0.0001096864,0.001592556],"genre_scores_gemma":[0.9972598,0.0006643873,0.001379992,0.0000613425,0.00001956836,0.00001426752,0.000001160757,0.000003950986,0.0005955255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6029964,"threshold_uncertainty_score":0.3080267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436527492191602,"score_gpt":0.2143643277356092,"score_spread":0.1999990528136932,"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."}}