{"id":"W4213165361","doi":"10.1002/rra.3951","title":"Principles for scientists working at the river science‐policy interface","year":2022,"lang":"en","type":"article","venue":"River Research and Applications","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia; University of Canberra; Griffith University; La Trobe University; University of Melbourne; Commonwealth Scientific and Industrial Research Organisation","keywords":"Science policy; Context (archaeology); Corporate governance; Relevance (law); Space (punctuation); Interface (matter); Resource (disambiguation); Natural resource management; Natural (archaeology); Environmental resource management; Political science; Environmental planning; Natural resource; Business; Public administration; Computer science; Environmental science; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4003914,0.001778972,0.002854177,0.004883447,0.02977139,0.05423276,0.01504441,0.08130467,0.006405287],"category_scores_gemma":[0.269652,0.002921766,0.00378027,0.004584656,0.1552107,0.04562256,0.0433321,0.07603323,0.003877444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01834026,"about_ca_system_score_gemma":0.08080968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007992837,"about_ca_topic_score_gemma":0.005151296,"domain_scores_codex":[0.6133968,0.2953337,0.01407328,0.02148892,0.03980023,0.015907],"domain_scores_gemma":[0.5697193,0.3436169,0.01044242,0.02368963,0.03382418,0.01870749],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002143238,0.00008606431,0.0003535306,0.0002185675,0.00002707542,0.0002347982,0.0327115,0.0006646023,0.0002079943,0.9391432,0.01883691,0.007494231],"study_design_scores_gemma":[0.00004643701,0.00003344123,0.00008932635,0.0006428375,0.00001669675,0.00008574494,0.008979886,0.000501231,0.0001733429,0.8269875,0.1623917,0.0000518495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002914754,0.002837023,0.06995212,0.8794053,0.003054839,0.0008105296,0.0000484075,0.0001409429,0.04083613],"genre_scores_gemma":[0.3279593,0.004794047,0.2498014,0.3764268,0.005374426,0.009139157,0.0001047245,0.0004247954,0.02597523],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5996086,"threshold_uncertainty_score":0.7394242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08304183321533114,"score_gpt":0.3729803691144644,"score_spread":0.2899385358991333,"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."}}