{"id":"W4378673036","doi":"10.54097/hset.v50i.8489","title":"An Investigation of Canadian Greenhouse Climate Prediction using Time Series","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Greenhouse gas; Arable land; Agriculture; Global warming; Environmental science; Climate change; Natural resource economics; Carbon dioxide equivalent; Population; China; Autoregressive integrated moving average; Greenhouse effect; Agricultural economics; Environmental protection; Time series; Geography; Economics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001392542,0.000700055,0.0003081941,0.0009399726,0.0008864145,0.001180144,0.0009559449,0.0004051439,0.001232767],"category_scores_gemma":[0.004131099,0.0002493426,0.0006695843,0.002226464,0.0002295785,0.000560194,0.0003196631,0.0006241614,0.0001745125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009233283,"about_ca_system_score_gemma":0.01019418,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.979102,"about_ca_topic_score_gemma":0.9692625,"domain_scores_codex":[0.9995597,0.0000661676,0.00002402546,0.000125449,0.0001628985,0.00006168605],"domain_scores_gemma":[0.9982585,0.0005618463,0.00008975149,0.00008502834,0.0009471116,0.0000577111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003448634,0.0002021261,0.1842001,0.0002680974,0.0003164242,0.0002487044,0.0002081529,0.7314426,0.001726803,0.003932058,0.0112008,0.06590915],"study_design_scores_gemma":[0.0000238361,0.00003303699,0.06781196,0.00002288453,0.00005337692,0.00001980006,0.0001440722,0.92571,0.0008305443,0.0003589146,0.004960109,0.00003149496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9570631,0.001271815,0.01373262,0.001450986,0.0001213533,0.00009802309,0.01577144,0.000780188,0.009710439],"genre_scores_gemma":[0.9756884,0.0007947694,0.00964415,0.0001007359,0.00001622397,0.00003729547,0.01169941,0.00004237806,0.001976688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02089804,"threshold_uncertainty_score":0.06699246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005224243089361473,"score_gpt":0.1789135485034039,"score_spread":0.1736893054140424,"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."}}