{"id":"W2015262509","doi":"10.1016/j.agrformet.2008.10.016","title":"Spatial modelling of photosynthesis for a boreal mixedwood forest by integrating micrometeorological, lidar and hyperspectral remote sensing data","year":2008,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Forest Research Institute; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photosynthetically active radiation; Environmental science; Canopy; Atmospheric sciences; Black spruce; Lidar; Taiga; Boreal; Spatial variability; Remote sensing; Tree canopy; Geography; Forestry; Photosynthesis; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001946036,0.0002060981,0.0003357938,0.00002675846,0.0003280567,0.00001952568,0.0001954518,0.0001532982,0.00000729964],"category_scores_gemma":[0.00008219246,0.0001318516,0.0000564841,0.0001273273,0.0005343431,0.0001255499,0.0002023243,0.0001434988,0.000002645477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002064552,"about_ca_system_score_gemma":0.000006690209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006344281,"about_ca_topic_score_gemma":0.001473854,"domain_scores_codex":[0.9986454,0.00006194857,0.0003008202,0.0005432669,0.000113905,0.0003346436],"domain_scores_gemma":[0.9991838,0.0002768818,0.0001482799,0.0002589772,0.00002318966,0.0001089216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004731205,0.0002256162,0.02233241,0.00006992334,0.0002564208,0.00002010072,0.002854872,0.001887127,0.7811493,0.0004410867,0.005254817,0.1850352],"study_design_scores_gemma":[0.002446281,0.001905722,0.2482367,0.00006487854,0.0005152643,0.002592057,0.001398562,0.6914379,0.03458039,0.007283083,0.008104039,0.001435101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9656525,0.0001785765,0.03278074,0.0004778819,0.00003122084,0.0003024726,0.00007463548,0.00003150953,0.0004704972],"genre_scores_gemma":[0.9127023,0.0001757424,0.08678246,0.00006684892,0.00005387079,0.000001007498,0.0001585348,0.00001002868,0.00004924456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.746569,"threshold_uncertainty_score":0.9590697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740350990699189,"score_gpt":0.2197927931571901,"score_spread":0.1923892832501982,"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."}}