{"id":"W2993872008","doi":"10.1038/s41597-019-0300-6","title":"Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution","year":2019,"lang":"en","type":"article","venue":"Scientific Data","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":660,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Australian Research Council; Department of Education and Training; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Compendium; Structural basin; Drainage basin; Land cover; Hydrology (agriculture); Environmental science; Spatial analysis; Computer science; Environmental resource management; Land use; Database; Remote sensing; Geography; Cartography; Geology; Ecology","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000591943,0.0001403017,0.0001412417,0.00002493185,0.0004418435,0.00006735733,0.0005538641,0.00005808804,0.001255416],"category_scores_gemma":[0.00001873302,0.0001276771,0.00001727444,0.0001154213,0.001086919,0.0004198123,0.003891431,0.000062671,0.004231803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000190963,"about_ca_system_score_gemma":0.000003377731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004261335,"about_ca_topic_score_gemma":0.0003161995,"domain_scores_codex":[0.9982145,0.00005962846,0.0001672582,0.0008893519,0.0003363679,0.0003328291],"domain_scores_gemma":[0.9988104,0.00001479881,0.00007627899,0.001021289,0.00000159165,0.0000756555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005984955,0.00008999251,0.9120167,0.000009799681,0.00002495529,0.00001322123,0.0001548409,0.00003890341,0.003569285,0.00005657278,0.07825549,0.005710395],"study_design_scores_gemma":[0.0003449432,0.00003625896,0.8942804,0.000005173582,0.00003348536,0.000005865954,0.00001831235,0.002990055,0.0001449704,0.0002938725,0.1016744,0.0001721975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946918,0.00002467337,0.0001293454,0.0003809076,0.001019086,0.0002392623,0.001703411,0.00003132131,0.001780159],"genre_scores_gemma":[0.9929016,0.00002455488,0.0001869565,0.0001223049,0.00003345637,0.000003300257,0.002610924,0.000006324856,0.004110554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02341895,"threshold_uncertainty_score":0.9996576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01102404109151187,"score_gpt":0.2050639716969465,"score_spread":0.1940399306054346,"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."}}