{"id":"W4404408725","doi":"10.1016/j.jag.2024.104267","title":"White blanket, blue waters: Tracing El Niño footprints in Canada","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration","keywords":"Blanket; White (mutation); Geography; Cartography; Geology; Oceanography; Archaeology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002782195,0.000353086,0.0002279053,0.002505219,0.001309068,0.001166272,0.0005698864,0.0003184552,0.00128531],"category_scores_gemma":[0.00117449,0.000180207,0.0002301468,0.00474253,0.0002985801,0.0003643903,0.0005868541,0.0003267745,0.0002343911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01100707,"about_ca_system_score_gemma":0.01123478,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9936802,"about_ca_topic_score_gemma":0.9963193,"domain_scores_codex":[0.9997792,0.00001177425,0.000008729759,0.00004455215,0.00007920632,0.00007651818],"domain_scores_gemma":[0.9993455,0.00004914099,0.00005090554,0.00002518566,0.0004094032,0.0001198221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002470385,0.0001027167,0.8737776,0.0001221548,0.0001629267,0.0004351323,0.001508387,0.03014747,0.002570997,0.002487737,0.02368577,0.06475209],"study_design_scores_gemma":[0.00002306922,0.00001771618,0.8759168,0.00011194,0.00004510286,0.00006662173,0.003428762,0.09360233,0.001064947,0.0005333,0.02513727,0.00005219758],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9480115,0.001026193,0.001339161,0.0004201468,0.00003220845,0.00008397553,0.03503467,0.000247816,0.01380423],"genre_scores_gemma":[0.9764082,0.000595644,0.003659174,0.00006573561,0.000005286709,0.00003184762,0.0155438,0.00003933414,0.003650982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01100707,"threshold_uncertainty_score":0.07986224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185507198292526,"score_gpt":0.2042573767906592,"score_spread":0.192402304807734,"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."}}