{"id":"W2210870558","doi":"10.1002/hyp.10783","title":"Field study on drainage densities and rescaled width functions in a high‐altitude alpine catchment","year":2016,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Competence Center Environment and Sustainability; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Drainage density; Drainage basin; Channel (broadcasting); Drainage; Hydrology (agriculture); Geology; Altitude (triangle); Channelized; Field (mathematics); Geomorphology; Geometry; Geography; Structural basin; Cartography; Mathematics; Computer science; Geotechnical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002302619,0.0001551552,0.0001956226,0.00004594574,0.0001800567,0.00001316407,0.0001657252,0.00006601404,0.0004313191],"category_scores_gemma":[0.0002931626,0.00008687348,0.00001570811,0.000153403,0.0002698271,0.0001203066,0.0004212946,0.00009943502,0.0001976898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004026232,"about_ca_system_score_gemma":0.00000299615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001598144,"about_ca_topic_score_gemma":0.0007880319,"domain_scores_codex":[0.998874,0.00007787509,0.0001748813,0.0004287975,0.0001593966,0.000285041],"domain_scores_gemma":[0.9994398,0.0002645301,0.00003913477,0.0001999597,0.000005493891,0.00005111008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005768757,0.001261167,0.9900771,0.00003588113,0.00005736881,0.0001903087,0.001238055,0.0003409887,0.0004940204,0.0003439157,0.003482454,0.001901819],"study_design_scores_gemma":[0.002810547,0.005747464,0.955367,0.00005364167,0.00006391546,0.000007160013,0.0006380064,0.0000561191,0.0006058424,0.03087492,0.003314031,0.0004613963],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826168,0.00003962562,0.0001890056,0.01158598,0.00004721016,0.0003449072,0.000001774219,0.00006358838,0.005111066],"genre_scores_gemma":[0.9968583,0.00004807599,0.00004345363,0.001578174,0.00002237848,0.0001316344,9.579408e-7,0.000005082993,0.001311967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03471019,"threshold_uncertainty_score":0.4722641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01367608780409948,"score_gpt":0.2335032230774768,"score_spread":0.2198271352733774,"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."}}