{"id":"W2913470492","doi":"10.1016/j.catena.2019.02.006","title":"The influence of landscape characteristics on the spatial variability of river temperatures","year":2019,"lang":"en","type":"article","venue":"CATENA","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Hydrology (agriculture); Tributary; Watershed; Physiographic province; Elevation (ballistics); Geology; Silt; Channel (broadcasting); STREAMS; Vegetation (pathology); Wetland; Groundwater; Digital elevation model; Environmental science; Geomorphology; Ecology; Geography; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003217364,0.00007571448,0.0001298556,0.0004584709,0.0002445406,0.000888603,0.0001793478,0.0002113662,0.002322418],"category_scores_gemma":[0.001476619,0.0001122194,0.00022926,0.0006193618,0.0004218991,0.0003054674,0.0003089245,0.0001619168,0.0001703025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003865206,"about_ca_system_score_gemma":0.0002428329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01975685,"about_ca_topic_score_gemma":0.05652123,"domain_scores_codex":[0.9997963,0.00008267065,0.00001199528,0.00005293667,0.00002058636,0.00003540394],"domain_scores_gemma":[0.9989605,0.0005507484,0.0001568846,0.00008271608,0.0001430221,0.0001062246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002130936,0.00002810026,0.9894522,0.00001581948,0.0001147538,0.000108064,0.0002540349,0.0009693242,0.003886756,0.0002475822,0.00012711,0.004583021],"study_design_scores_gemma":[0.000001112043,0.000008915556,0.999128,0.000001379526,0.00001137465,0.00001490444,0.0000971987,0.0005316205,0.00006680861,0.00002377364,0.0001132246,0.000001623262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984872,0.00006324903,0.00005817012,0.00002866361,0.000001873917,0.000001203871,0.0001124649,0.000002518389,0.001244602],"genre_scores_gemma":[0.999728,0.00001721549,0.00002240322,0.00000351725,0.000001427434,9.728109e-7,0.00005909391,0.000001805165,0.0001654869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01975685,"threshold_uncertainty_score":0.03928369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0125052092796107,"score_gpt":0.2024947414402441,"score_spread":0.1899895321606334,"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."}}