{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001711922,0.0002041512,0.0004620958,0.000282079,0.0003427377,0.0007328148,0.0006029395,0.000336162,0.0006739579],"category_scores_gemma":[0.00630824,0.0001605346,0.0003558226,0.0002246703,0.001069227,0.0006135125,0.0006555839,0.0005941452,0.00007536088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003586619,"about_ca_system_score_gemma":0.0004465507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001326561,"about_ca_topic_score_gemma":0.0015083,"domain_scores_codex":[0.9991904,0.0003328268,0.00005738435,0.0001783845,0.0001362072,0.0001046778],"domain_scores_gemma":[0.9946437,0.003413432,0.0005871478,0.0008030853,0.0003722314,0.0001804583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00349139,0.004234632,0.1134308,0.0005880435,0.0004690384,0.0005229675,0.002056182,0.2377124,0.5731195,0.006291209,0.001086992,0.05699672],"study_design_scores_gemma":[0.0004503899,0.01793846,0.1290884,0.00009060007,0.0003620617,0.0003309831,0.001289879,0.5764859,0.2566162,0.01481104,0.002242054,0.0002940092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930548,0.00002753141,0.006461751,0.0000215301,0.000008384137,0.00004302903,0.00007705169,0.0000250339,0.0002809524],"genre_scores_gemma":[0.9970967,0.00001622906,0.002653212,0.00001065649,0.000003699906,0.00007202874,0.00005711271,0.000008231531,0.00008227933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001711922,"threshold_uncertainty_score":0.009053588,"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."}}