{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002960522,0.000701299,0.0004489746,0.0003432851,0.001235121,0.0008184111,0.002256388,0.0006619245,0.003328427],"category_scores_gemma":[0.0007325236,0.0003972843,0.000547496,0.0004271499,0.0008701348,0.00047268,0.0006459203,0.0006685398,0.0002521677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00347639,"about_ca_system_score_gemma":0.006343134,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6667283,"about_ca_topic_score_gemma":0.6852494,"domain_scores_codex":[0.9998819,0.00001532368,0.00000407352,0.00002973822,0.00002676106,0.0000421942],"domain_scores_gemma":[0.9997703,0.00005493041,0.00001351316,0.00002216145,0.00008899979,0.00005012989],"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":[0.00007151376,0.00006620827,0.002526304,0.00002138578,0.00002244095,0.00005108948,0.00005931543,0.9867687,0.002285626,0.001554116,0.0007798477,0.005793532],"study_design_scores_gemma":[0.00002352148,0.00001225121,0.0004385362,0.000001367771,0.000005601175,0.000003729437,0.00001868603,0.998522,0.0003761022,0.0002268112,0.0003663999,0.000004922838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8149028,0.0002593977,0.1615659,0.0004258599,0.0001187319,0.0003092035,0.00138829,0.003580821,0.01744903],"genre_scores_gemma":[0.9198967,0.00008586825,0.07621396,0.00006629141,0.00001514285,0.0001295811,0.0005668305,0.0001787197,0.002846849],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3332717,"threshold_uncertainty_score":0.6704687,"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."}}