{"id":"W4385297189","doi":"10.1016/j.dib.2023.109450","title":"Heating degree day spatial datasets for Canada","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Agriculture and Agri-Food Canada; U.S. Forest Service; Natural Resources Canada; Environment and Climate Change Canada; Great Lakes Fishery Commission","keywords":"Heating degree day; Degree (music); Degree day; Environmental science; Climate change; Statistics; Data set; Mean absolute error; Climatology; Meteorology; Geography; Mathematics; Mean squared error; Energy consumption; Geology; Ecology","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":[0.0002775452,0.0006405978,0.0004105852,0.002041792,0.001344634,0.0009919164,0.001168917,0.0003403867,0.01116915],"category_scores_gemma":[0.001400211,0.0002768615,0.0005734977,0.007309162,0.0002673041,0.0003248593,0.0004984501,0.0005994864,0.003499041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01264872,"about_ca_system_score_gemma":0.01818065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9863921,"about_ca_topic_score_gemma":0.992059,"domain_scores_codex":[0.9995775,0.00002200513,0.00001988717,0.00006729654,0.0002114733,0.0001018133],"domain_scores_gemma":[0.9979663,0.00006275825,0.00008056919,0.0001460495,0.001601427,0.0001429294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002818348,0.00008891641,0.05383692,0.0004338298,0.0001572486,0.0001617511,0.0003102541,0.0217581,0.00133373,0.003603276,0.881957,0.03607714],"study_design_scores_gemma":[0.0001358666,0.00002180231,0.2046115,0.000210719,0.00005795873,0.0001073617,0.0006914401,0.01677722,0.002393125,0.001077656,0.7738035,0.0001118008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009616404,0.0001515279,0.0007139138,0.0001279631,0.0000227401,0.00004626146,0.984378,0.0004170553,0.004525999],"genre_scores_gemma":[0.02703765,0.0002468286,0.002841012,0.00004547579,0.000006578356,0.0001009161,0.9653209,0.00009400311,0.004306654],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01360786,"threshold_uncertainty_score":0.09177327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812790325536761,"score_gpt":0.2456830122117347,"score_spread":0.2175551089563671,"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."}}