{"id":"W4407953429","doi":"10.5751/es-15586-300123","title":"Facilitating convergence research on water resource management with a collaborative, adaptive, and multi-scale systems thinking framework","year":2025,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Scale (ratio); Convergence (economics); Environmental resource management; Adaptive management; Resource (disambiguation); Resource management (computing); Knowledge management; Computer science; Business; Management science; Environmental science; Geography; Engineering; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007229477,0.0001267696,0.0001709319,0.00002383212,0.001107568,0.00003249469,0.0001174594,0.0001247789,0.00002882382],"category_scores_gemma":[0.00001484487,0.00008617149,0.00001855339,0.0002274488,0.0008313764,0.00006860599,0.0005188952,0.0002700733,0.00001599861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001131594,"about_ca_system_score_gemma":0.000005748764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008231847,"about_ca_topic_score_gemma":0.0002002862,"domain_scores_codex":[0.9986142,0.0002571543,0.0001284784,0.000434704,0.0001955837,0.0003698805],"domain_scores_gemma":[0.9993407,0.0004265964,0.00002485698,0.0001412865,0.00002168591,0.00004487305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007938949,0.0007541942,0.4190879,0.0006801985,0.001623915,0.0001282273,0.2738812,0.009960179,0.001868509,0.2425009,0.04656311,0.002157803],"study_design_scores_gemma":[0.00298354,0.001819404,0.4285376,0.0006025066,0.0001064782,0.000009118778,0.4815249,0.01901333,0.001886855,0.03937205,0.02325389,0.0008902993],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393709,0.0004054247,0.003299847,0.001313591,0.0001590776,0.0006591767,0.00001717478,0.00006250244,0.05471229],"genre_scores_gemma":[0.9844393,0.00006210351,0.01012537,0.00042271,0.000009212978,0.0001201576,0.000001581676,0.000006363632,0.004813267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2076437,"threshold_uncertainty_score":0.8518628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02318253586456907,"score_gpt":0.2792442743177084,"score_spread":0.2560617384531393,"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."}}