{"id":"W2420445840","doi":"10.1177/0265813516654473","title":"An approach to maintaining hydrological networks in the face of land use change","year":2016,"lang":"en","type":"article","venue":"Environment and Planning B Urban Analytics and City Science","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Ephemeral key; Hydrology (agriculture); Drainage; Land use; Environmental resource management; Drainage system (geomorphology); Environmental science; Geography; Water resource management; Civil engineering; Geology; Computer science; Engineering; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008927234,0.00009512072,0.0001197839,0.00004067482,0.0001534227,0.00006922027,0.0002615116,0.00003876041,0.00002680559],"category_scores_gemma":[0.00001220531,0.00004839366,0.00001223757,0.0001734052,0.0001747168,0.0003446661,0.0001428581,0.00005962161,0.000003377612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002175436,"about_ca_system_score_gemma":0.000001832607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000183153,"about_ca_topic_score_gemma":0.00002825516,"domain_scores_codex":[0.999014,0.00004088488,0.0001304533,0.0003188648,0.0002420741,0.0002537282],"domain_scores_gemma":[0.9995736,0.0000754449,0.00004937511,0.0001812725,0.000001321,0.000119011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009052544,0.00003205971,0.991495,0.000001578245,0.000001438703,0.00000222863,0.001637022,0.006066337,0.0001363256,0.00003798335,0.00001429104,0.0005666672],"study_design_scores_gemma":[0.0001227008,0.00009561826,0.9097509,0.0000172538,0.000006386435,0.000003736756,0.0003175429,0.08918786,0.00001378246,0.00004104298,0.0003463264,0.00009679508],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973665,0.00005231079,0.001959348,0.0001562958,0.0000135144,0.0001242084,0.000003895217,0.000004474796,0.0003194693],"genre_scores_gemma":[0.9994174,0.00005571928,0.0002006942,0.000274431,0.00002778859,0.000005927908,0.000001386691,0.000002568877,0.00001403305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08312152,"threshold_uncertainty_score":0.1973436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0447551792742555,"score_gpt":0.2295924721259403,"score_spread":0.1848372928516848,"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."}}