{"id":"W4402143095","doi":"10.1007/s11356-024-34652-5","title":"Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data","year":2024,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Climate variability and models","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"National Science and Technology Council","keywords":"Precipitation; Environmental science; Agriculture; Coupled model intercomparison project; Climate change; Agricultural productivity; Climatology; Production (economics); Climate model; Meteorology; Geography; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001385453,0.0006447549,0.0003587387,0.001097784,0.0003612823,0.0008557282,0.0007270492,0.0009356621,0.001449494],"category_scores_gemma":[0.004086626,0.0003908233,0.001046654,0.002100811,0.0002380226,0.001048995,0.0005033063,0.001086814,0.0004649776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005969029,"about_ca_system_score_gemma":0.0004980915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01876721,"about_ca_topic_score_gemma":0.01720248,"domain_scores_codex":[0.9996569,0.0000954253,0.00002888092,0.0001277857,0.00004613177,0.00004487152],"domain_scores_gemma":[0.9984252,0.0008418658,0.0001972601,0.0002004609,0.0002382644,0.00009696976],"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":[0.0003625708,0.0001499389,0.2557731,0.0000605058,0.0004026849,0.0001232017,0.00005815069,0.7042243,0.001567575,0.0008013067,0.002492352,0.03398419],"study_design_scores_gemma":[0.00001887684,0.0000204878,0.08814689,0.000009880042,0.00003988916,0.00003085172,0.00004032258,0.908987,0.0008359321,0.001138299,0.0007084765,0.00002305338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973369,0.0002293508,0.01498128,0.0005646413,0.00007723221,0.00002486,0.008507145,0.0004350379,0.001811416],"genre_scores_gemma":[0.9824272,0.00009363037,0.009153119,0.00003394963,0.0000538169,0.00002451717,0.007816165,0.00003132859,0.0003662576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01876721,"threshold_uncertainty_score":0.03731596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1350584514976705,"score_gpt":0.3286369771162783,"score_spread":0.1935785256186078,"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."}}