{"id":"W4385975854","doi":"10.1016/j.agrformet.2023.109657","title":"Crop water use efficiency from eddy covariance methods in cold water-limited regions","year":2023,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"","keywords":"Eddy covariance; Water-use efficiency; Evapotranspiration; Environmental science; Agronomy; Water use; Crop yield; Transpiration; Irrigation; Ecosystem; Ecology; Biology; Photosynthesis","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002171805,0.0001518493,0.0001895649,0.0000523502,0.000150242,0.0000511995,0.000152964,0.0001295562,0.0001286098],"category_scores_gemma":[0.00001835098,0.00007198744,0.00004238143,0.0002214751,0.0002027594,0.0002293109,0.0002216782,0.0001535983,0.0003070721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003542817,"about_ca_system_score_gemma":0.000001410145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002526785,"about_ca_topic_score_gemma":0.002004507,"domain_scores_codex":[0.9987615,0.0001470251,0.0002200935,0.000345905,0.00009599612,0.0004295219],"domain_scores_gemma":[0.9996247,0.0001223145,0.00002864728,0.0001371027,0.000007772855,0.00007950947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000624006,0.0001244814,0.07181236,0.00000632771,0.0000519113,0.0001197661,0.003320759,0.1007143,0.8147464,0.006721228,0.001232145,0.001087906],"study_design_scores_gemma":[0.001345913,0.0002706913,0.8620812,0.0000177901,0.00009608443,0.0001008981,0.000148209,0.04882988,0.01744987,0.008520396,0.06032465,0.0008144009],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973649,0.00001742061,0.0007606575,0.001091572,0.0001924984,0.0001695876,0.00002739834,0.00006905595,0.0003069598],"genre_scores_gemma":[0.9939342,0.00005530747,0.003211443,0.000187488,0.00002860087,0.00003692531,0.0003372565,0.000007041574,0.002201749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7972965,"threshold_uncertainty_score":0.3946894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01600016386945293,"score_gpt":0.2350698217047551,"score_spread":0.2190696578353022,"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."}}