{"id":"W4383982174","doi":"10.5194/gmd-16-3809-2023","title":"Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress","year":2023,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Global Institute for Water Security","funders":"Global Institute for Water Security, University of Saskatchewan; Global Water Futures; National Institute of Food and Agriculture; National Oceanic and Atmospheric Administration; National Science Foundation","keywords":"Environmental science; Growing season; Yield (engineering); Sowing; Crop; Climate change; Agriculture; Leaf area index; Crop yield; Climate model; Scale (ratio); Geography; Climatology; Agronomy; Atmospheric sciences; Ecology; Biology; Forestry","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":[0.0003397174,0.0006501008,0.0004439295,0.0002469876,0.0005853,0.0007011062,0.001549368,0.000854791,0.001623701],"category_scores_gemma":[0.0008051873,0.0004487102,0.0007584156,0.0003086228,0.0003117325,0.0005210698,0.0004671648,0.0006678483,0.0002976822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521874,"about_ca_system_score_gemma":0.002242822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2393196,"about_ca_topic_score_gemma":0.1561506,"domain_scores_codex":[0.9999088,0.00002782942,0.000004999958,0.00002385856,0.00001572965,0.00001878095],"domain_scores_gemma":[0.999819,0.00006367866,0.00001696323,0.00001879844,0.00005660814,0.00002502741],"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":[0.00004561141,0.00003338081,0.003419537,0.00001467067,0.00002545788,0.00003355873,0.00002097568,0.9926952,0.0008500904,0.000340724,0.000400808,0.002119938],"study_design_scores_gemma":[0.00001208231,0.00000707771,0.0005966536,9.622787e-7,0.000004397899,0.000001742126,0.000006679571,0.9990444,0.0001110007,0.00006401088,0.0001480917,0.000002838701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9458866,0.0001273856,0.03906033,0.0004721906,0.0000695085,0.0001518921,0.002461946,0.001221633,0.01054863],"genre_scores_gemma":[0.9789955,0.00005597459,0.01754076,0.00005594678,0.00001204227,0.00008952105,0.00123117,0.00009361366,0.001925467],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2393196,"threshold_uncertainty_score":0.4758533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.020571195756645,"score_gpt":0.2343829416510825,"score_spread":0.2138117458944375,"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."}}