{"id":"W2477164700","doi":"10.1080/11956860.2016.1202885","title":"Applying time series models to estimate time lags between sap flux and micro-meteorological factors","year":2016,"lang":"en","type":"article","venue":"Ecoscience","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Transpiration; Environmental science; Canopy; Vapour Pressure Deficit; Atmospheric sciences; Canopy conductance; Stomatal conductance; Flux (metallurgy); Univariate; Ecology; Mathematics; Multivariate statistics; Statistics; Botany; Biology; Chemistry; Photosynthesis; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001825859,0.0009695062,0.0006109125,0.001026491,0.0002468537,0.0008104031,0.000805106,0.0006893993,0.002190198],"category_scores_gemma":[0.004849325,0.0004357973,0.0009771195,0.001363329,0.0002464915,0.0008774825,0.0004250207,0.001022094,0.000587811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004754664,"about_ca_system_score_gemma":0.0006484369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01306698,"about_ca_topic_score_gemma":0.01019194,"domain_scores_codex":[0.9996,0.0001297984,0.00003042438,0.0001451398,0.00005840531,0.00003625341],"domain_scores_gemma":[0.998149,0.001358805,0.0002403121,0.0001113027,0.0001090532,0.00003139146],"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":[0.0001359719,0.0001406788,0.0281307,0.0001374365,0.0003834807,0.0001131548,0.0001094214,0.8652055,0.002963515,0.007310869,0.0009883114,0.09438083],"study_design_scores_gemma":[0.000004009896,0.00002072747,0.002498506,0.000005137321,0.00001649359,0.0000108084,0.00001137926,0.9946654,0.0003584714,0.002062995,0.0003367514,0.000009284088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1676724,0.0003427384,0.8282669,0.0001779484,0.0001214691,0.00008301646,0.001019061,0.0009971628,0.001319442],"genre_scores_gemma":[0.8800228,0.0004497239,0.1140168,0.00006472051,0.0000680189,0.0002187891,0.001682848,0.0001127104,0.00336368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01306698,"threshold_uncertainty_score":0.02598184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304256989085149,"score_gpt":0.2152995447602581,"score_spread":0.2022569748694066,"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."}}