{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002805315,0.0000963389,0.00009665704,0.000003193254,0.00008681358,0.0000143652,0.0005876075,0.00003112871,0.000342825],"category_scores_gemma":[0.000084295,0.0000982413,0.000009200331,0.0001611612,0.00006857814,0.0002056039,0.0009708238,0.00006880306,0.00006509971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224737,"about_ca_system_score_gemma":0.00002394615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7909448,"about_ca_topic_score_gemma":0.8351955,"domain_scores_codex":[0.9989288,0.00001815697,0.0001612828,0.0003703522,0.0002186151,0.0003027925],"domain_scores_gemma":[0.9991536,0.00008307447,0.00003620598,0.0006583101,4.482262e-7,0.00006834203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002549852,0.00006537532,0.2998303,0.00002192268,0.00001363384,0.00009719971,0.0001297589,0.06747748,0.0004186071,0.00004488894,0.3519975,0.2798779],"study_design_scores_gemma":[0.0003111451,0.00001616865,0.4289666,0.000007142924,0.000005621281,0.000002685181,0.0000927892,0.3024385,0.00001858595,0.00006938975,0.2678945,0.0001768434],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216226,0.00003727227,0.05518354,0.001445708,0.000875932,0.001127158,0.01643121,0.0001395483,0.003136979],"genre_scores_gemma":[0.9276167,0.00005371963,0.03226914,0.00179747,0.0001499649,0.00007114817,0.03702321,0.00006075103,0.0009578946],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.279701,"threshold_uncertainty_score":0.4006164,"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."}}