{"id":"W2911761601","doi":"10.5194/gmd-12-4075-2019","title":"Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH","year":2019,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of British Columbia","funders":"Jenny ja Antti Wihurin Rahasto; NordForsk; Academy of Finland","keywords":"Stomatal conductance; Environmental science; Scots pine; Evapotranspiration; Calibration; FluxNet; Population; Atmospheric sciences; Taiga; Black spruce; Evergreen; Boreal; Mathematics; Photosynthesis; Ecology; Botany; Statistics; Ecosystem; Biology; Pinus <genus>; Physics","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":[0.001862888,0.0006043219,0.00054461,0.0003258879,0.0002781405,0.0005602933,0.0008765866,0.0006846413,0.0005297697],"category_scores_gemma":[0.003850779,0.0003252091,0.0006061161,0.0002693181,0.0004003141,0.0006122415,0.0005649555,0.0006729795,0.00008268145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008237059,"about_ca_system_score_gemma":0.000723122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02189552,"about_ca_topic_score_gemma":0.01315659,"domain_scores_codex":[0.9997296,0.0001345021,0.00001649051,0.0000540899,0.00003950868,0.00002584394],"domain_scores_gemma":[0.998709,0.0008769924,0.00008811195,0.0001028609,0.0001802139,0.00004286316],"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":[0.00004923457,0.0000264893,0.005279142,0.00001803511,0.00002741987,0.00001660687,0.00002604721,0.9898488,0.0007657573,0.0005168227,0.00007414967,0.003351514],"study_design_scores_gemma":[0.0000100746,0.00001148681,0.0006921935,0.000001882592,0.000004184159,0.000003297353,0.000003851203,0.9988242,0.0002582585,0.0001428857,0.00004421944,0.000003458056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9008947,0.0001726009,0.09644929,0.0001538414,0.00002277749,0.00004386972,0.000232234,0.0005023474,0.001528344],"genre_scores_gemma":[0.9897761,0.00002076056,0.009825878,0.00001963443,0.000003731772,0.00003305087,0.0001470198,0.00003949677,0.0001342703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02189552,"threshold_uncertainty_score":0.04353619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02417769073040901,"score_gpt":0.2292278364362489,"score_spread":0.2050501457058399,"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."}}