{"id":"W2801265418","doi":"10.5194/bg-15-6885-2018","title":"An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework","year":2018,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Leaf area index; Environmental science; Deciduous; Terrestrial ecosystem; Primary production; Ecosystem; Biosphere; Atmospheric sciences; Carbon cycle; Phenology; FluxNet; Biosphere model; Seasonality; Growing season; Productivity; Vegetation (pathology); Climatology; Ecology; Biology; Eddy covariance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000439935,0.0008186205,0.0003516006,0.0004069211,0.0007102989,0.0009724217,0.001589032,0.0005698593,0.001580824],"category_scores_gemma":[0.0009608731,0.0002403915,0.0006014205,0.0007275867,0.000258835,0.0005995401,0.0004752487,0.0006045615,0.0001841406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004821384,"about_ca_system_score_gemma":0.006033897,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8637739,"about_ca_topic_score_gemma":0.8373694,"domain_scores_codex":[0.9998012,0.00003412504,0.000009440011,0.00004888298,0.00006143474,0.00004503603],"domain_scores_gemma":[0.9996817,0.00005744017,0.00002989276,0.00002632749,0.0001680558,0.00003663565],"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":[0.00004927997,0.00003832576,0.007423401,0.00003720507,0.00004614156,0.00003768774,0.00004552621,0.9788201,0.002118578,0.002286911,0.001217083,0.007879728],"study_design_scores_gemma":[0.00001144442,0.000005999461,0.0029961,0.000003576171,0.00001183117,0.0000079254,0.00001063128,0.9949811,0.00036874,0.0002357766,0.001353907,0.00001298973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7543579,0.0008360521,0.2083115,0.0007561588,0.0001820675,0.0002297829,0.01028789,0.002344077,0.02269457],"genre_scores_gemma":[0.9569321,0.000193923,0.03784937,0.00006508561,0.0000132036,0.00008018786,0.002153776,0.0001588659,0.002553418],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1362261,"threshold_uncertainty_score":0.2740566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02326993378474902,"score_gpt":0.2257413094708715,"score_spread":0.2024713756861225,"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."}}