{"id":"W4384472185","doi":"10.1016/j.compag.2023.108057","title":"A modularized parallel distributed High–Performance computing framework for simulating seasonal frost dynamics in Canadian croplands","year":2023,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Frost (temperature); Scalability; Environmental science; Snow; Greenhouse gas; Computer science; Calibration; Snowmelt; Computation; Overwintering; Climate model; Meteorology; Climate change; Algorithm; Database; Mathematics; Ecology; Statistics","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":[0.0002472004,0.0001507682,0.0001917961,0.0000557488,0.0002765689,0.00003569776,0.0001479743,0.0001364853,0.000003916177],"category_scores_gemma":[0.00002071074,0.0001213566,0.00002483307,0.0004668959,0.00005163386,0.0000737394,0.0001573909,0.00028792,0.000005637561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004663312,"about_ca_system_score_gemma":0.00001166958,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005106326,"about_ca_topic_score_gemma":0.08242356,"domain_scores_codex":[0.9987311,0.00002588081,0.0001621309,0.0003044577,0.00008475406,0.0006916279],"domain_scores_gemma":[0.9996905,0.0001068116,0.00004182119,0.00007681435,0.000005337067,0.00007875557],"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":[0.00002688019,0.00001880534,0.4192304,0.00002449247,0.00002032762,0.00001421196,0.0002971657,0.5724251,0.00001128297,0.004553032,0.001347825,0.002030456],"study_design_scores_gemma":[0.000517838,0.0000462401,0.2373716,0.00003420312,0.00000521269,0.00000153968,0.00004927084,0.7576353,0.000002295669,0.003067306,0.001119713,0.0001495361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802925,0.00006831512,0.01756043,0.00152987,0.0001156968,0.0002921455,0.00002536994,0.00004163134,0.00007402518],"genre_scores_gemma":[0.9962816,0.0001087986,0.002896644,0.0002441342,0.00003387433,0.00001652107,0.0003635137,0.000006859601,0.00004806764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1852101,"threshold_uncertainty_score":0.9343198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004974808931227597,"score_gpt":0.2074286885820498,"score_spread":0.2024538796508222,"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."}}