{"id":"W4288431987","doi":"10.1002/essoar.10509195.2","title":"Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating model-agnostic and model-specific configuration steps in applications of large-domain hydrologic models","year":2022,"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 Calgary; University of Saskatchewan","funders":"U.S. Army Corps of Engineers; Bureau of Reclamation; Global Water Futures; National Science Foundation","keywords":"World Wide Web; Workflow; Computer science; Electronic mail; Database","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03085722,0.001171538,0.001090335,0.001967551,0.003246704,0.004380483,0.005528714,0.002019554,0.004415786],"category_scores_gemma":[0.05344252,0.001157723,0.003532658,0.002301177,0.002876353,0.007133449,0.01166524,0.004608441,0.002306601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002471203,"about_ca_system_score_gemma":0.008749448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01367024,"about_ca_topic_score_gemma":0.009767854,"domain_scores_codex":[0.9897637,0.004480367,0.001030919,0.002002707,0.002056187,0.0006661039],"domain_scores_gemma":[0.9432813,0.01107996,0.001613815,0.03462767,0.006219695,0.003177494],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001261574,0.001199649,0.02139273,0.0007531088,0.0006245903,0.001762256,0.0101021,0.1486007,0.02180861,0.3294704,0.09180042,0.3712239],"study_design_scores_gemma":[0.0003652661,0.0001724303,0.003016653,0.0003288881,0.0001362824,0.0003844673,0.0007393302,0.4314703,0.02321334,0.3595211,0.1802374,0.0004144957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01739927,0.0001138317,0.9478504,0.001175987,0.0001996427,0.0004828654,0.0009216088,0.02741817,0.004438269],"genre_scores_gemma":[0.1689718,0.0001889214,0.8109688,0.0006160264,0.0001225055,0.00119469,0.005749422,0.009040027,0.003147811],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9691428,"threshold_uncertainty_score":0.1631905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04345404389318584,"score_gpt":0.2923897154377067,"score_spread":0.2489356715445208,"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."}}