{"id":"W4399622659","doi":"10.22541/essoar.171829793.30234109/v1","title":"Convergent and transdisciplinary integration: On the future of integrated modeling of human-water systems","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Manitoba; Université du Québec à Montréal; Environment and Climate Change Canada; University of Waterloo; Global Institute for Water Security; Western University; Water Security Agency; University of Saskatchewan","funders":"","keywords":"Earth system science; Discipline; Systems science; Human systems engineering; Natural (archaeology); Management science; Interdependence; Complex system; Coproduction; Computer science; Planetary boundaries; Epistemology; Sociology; Political science; Ecology; Sustainable development; Engineering; Artificial intelligence; Social science; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01913427,0.00119284,0.002092235,0.001948473,0.002029859,0.00898815,0.003812152,0.004113917,0.003333956],"category_scores_gemma":[0.02764872,0.000738506,0.002259734,0.002643845,0.01642861,0.01864341,0.01181566,0.007116144,0.0004716509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004935964,"about_ca_system_score_gemma":0.006119927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009579972,"about_ca_topic_score_gemma":0.006417689,"domain_scores_codex":[0.9887506,0.008545423,0.0002518651,0.0006820589,0.001382837,0.0003871343],"domain_scores_gemma":[0.985033,0.01059793,0.0006846297,0.001584118,0.001223522,0.0008768189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002585039,0.00002506099,0.0006091325,0.00009399498,0.00005390774,0.00004782565,0.001257261,0.09056111,0.00009965118,0.8957056,0.0009158554,0.0106047],"study_design_scores_gemma":[0.000008549986,0.00001224597,0.0001186823,0.00009770803,0.00001511486,0.00001221178,0.000361263,0.1106233,0.00005888315,0.8802305,0.008443106,0.00001845873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02334019,0.01257135,0.866215,0.06013463,0.0006030556,0.00009933845,0.0001383455,0.0002954018,0.03660266],"genre_scores_gemma":[0.7264361,0.0141214,0.2497278,0.003521036,0.000823962,0.0004895105,0.0002741211,0.000299733,0.004306265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01913427,"threshold_uncertainty_score":0.1011929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208837592983746,"score_gpt":0.2459524518217259,"score_spread":0.2238640758918884,"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."}}