{"id":"W4392659163","doi":"10.5194/egusphere-egu24-16855","title":"Improving process understanding using multi-criteria model comparison for different catchments","year":2024,"lang":"en","type":"preprint","venue":"","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Process (computing); Computer science; Process management; Environmental resource management; Business; Environmental science; Programming language","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004434213,0.0005420492,0.0005955416,0.0002782973,0.0003475346,0.001298933,0.001207469,0.0003251073,0.000003935752],"category_scores_gemma":[0.00002859387,0.000480705,0.0002081586,0.0001486235,0.00003100196,0.0001952143,0.002070624,0.0007968517,0.000004233298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006582367,"about_ca_system_score_gemma":0.0004201645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001743341,"about_ca_topic_score_gemma":0.00002467404,"domain_scores_codex":[0.9970697,0.00005050342,0.0006229125,0.001243605,0.0003681676,0.0006451542],"domain_scores_gemma":[0.9986155,0.0001290339,0.0003100433,0.0006759085,0.0001042423,0.0001652638],"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":[0.00005422134,0.0003813778,0.001718743,0.01117383,0.000378718,0.00001978196,0.01683871,0.9333334,0.00382389,0.02618996,0.001361551,0.004725823],"study_design_scores_gemma":[0.0003231875,0.00003371108,0.000003884185,0.0009968095,0.00007437006,0.000002973189,0.0002110876,0.9474257,0.001279586,0.04907684,0.000004117188,0.000567728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02522549,0.0004147913,0.9706692,0.0002495963,0.0016981,0.000852469,0.00005729631,0.0006572496,0.0001758665],"genre_scores_gemma":[0.6984473,0.000001498193,0.3010101,0.00007122903,0.00008451053,0.00007704544,0.00004130338,0.0000423071,0.0002246242],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6732219,"threshold_uncertainty_score":0.9997644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2263910519668734,"score_gpt":0.3969314836353519,"score_spread":0.1705404316684785,"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."}}