{"id":"W2050979718","doi":"10.1109/tgrs.2013.2247405","title":"Improved LAI Algorithm Implementation to MODIS Data by Incorporating Background, Topography, and Foliage Clumping Information","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Leaf area index; Remote sensing; Land cover; Environmental science; FluxNet; Canopy; Algorithm; Mathematics; Ecosystem; Geography; Land use; Eddy covariance","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002968812,0.0001952573,0.0001388266,0.0001019232,0.0007188205,0.0004058506,0.000167456,0.00008163679,0.00002509577],"category_scores_gemma":[0.000006202741,0.0001641219,0.00002612139,0.0005710314,0.0002032338,0.002210936,0.00002795735,0.0001902219,0.00004538394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007865108,"about_ca_system_score_gemma":0.00001097081,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182818,"about_ca_topic_score_gemma":0.0008007307,"domain_scores_codex":[0.99853,0.00004932364,0.0002983511,0.0004738539,0.0003160507,0.0003324628],"domain_scores_gemma":[0.9992876,0.00004271408,0.0001099612,0.0003385351,0.00002981538,0.0001913549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002191867,0.000006995,0.000007925558,0.00000581231,0.000004366153,5.521164e-7,0.0004733705,0.0001349961,0.03808892,3.489252e-7,0.0002860875,0.9609885],"study_design_scores_gemma":[0.0003704492,0.0001387414,0.002958745,0.00006095016,0.00002717382,0.00008290733,0.003297826,0.9802144,0.01136061,0.0001561838,0.0009108118,0.0004211967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2767097,0.000004641453,0.7219226,0.0005272858,0.0001965679,0.0004138019,0.00002914798,0.00005020534,0.0001460243],"genre_scores_gemma":[0.6145167,0.00005087575,0.3843631,0.0008843446,0.00002746411,3.651486e-7,0.00003175,0.00001240565,0.000113006],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9800794,"threshold_uncertainty_score":0.9947521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166034234933623,"score_gpt":0.2439615601896276,"score_spread":0.2323012178402914,"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."}}