{"id":"W2902210878","doi":"10.1016/j.scitotenv.2018.11.361","title":"Climate-phenology-hydrology interactions in northern high latitudes: Assessing the value of remote sensing data in catchment ecohydrological studies","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; McMaster University; Trent University","funders":"H2020 European Research Council; European Research Council; Leverhulme Trust","keywords":"Environmental science; Normalized Difference Vegetation Index; Precipitation; Drainage basin; Climate change; Phenology; Northern Hemisphere; Streamflow; Climatology; Vegetation (pathology); Latitude; Hydrology (agriculture); Geography; Meteorology; Ecology; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001446111,0.0000952375,0.0001788805,0.00002721337,0.0004647895,0.00001492637,0.0008173618,0.00001956164,0.00006293268],"category_scores_gemma":[0.0001603537,0.00004366804,0.00002813794,0.0003608854,0.00288299,0.0001531317,0.0005876193,0.0001510278,0.00001356171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000363422,"about_ca_system_score_gemma":0.00003282956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004755956,"about_ca_topic_score_gemma":0.008663297,"domain_scores_codex":[0.9986984,0.0001481146,0.000320774,0.0002644724,0.0002866109,0.0002816065],"domain_scores_gemma":[0.9987609,0.0003343517,0.0001912929,0.0006808371,0.00001448507,0.00001816704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005726266,0.000126263,0.22332,0.00001715309,0.00008992419,0.000004124296,0.009137625,0.7232836,0.002770795,0.0003140532,0.00002962154,0.04084959],"study_design_scores_gemma":[0.00009754158,0.0000650131,0.8450372,0.00002458623,0.00001811615,0.000006710672,0.002240289,0.1510845,0.0001521047,0.001169915,0.00005132375,0.00005268179],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945779,0.0002999463,0.00003303779,0.004304673,0.0003533573,0.000186758,0.00001291506,0.000003649816,0.0002277535],"genre_scores_gemma":[0.9981171,0.0002069249,0.001545337,0.00007507256,0.00003577921,3.238577e-7,0.000002416404,0.000001556819,0.00001552434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6217172,"threshold_uncertainty_score":0.9998306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05101176695557116,"score_gpt":0.2915208209939297,"score_spread":0.2405090540383585,"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."}}