{"id":"W1977422739","doi":"10.1016/j.rse.2012.08.027","title":"Continuous observation of tree leaf area index at ecosystem scale using upward-pointing digital cameras","year":2012,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":178,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Leaf area index; Remote sensing; Environmental science; Moderate-resolution imaging spectroradiometer; Phenology; Ecosystem; Canopy; Scale (ratio); Vegetation (pathology); Forest ecology; Geography; Ecology; Satellite; Cartography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003873372,0.0003331437,0.0005148152,0.00005623392,0.0001453859,0.00002576523,0.0001604293,0.0001889746,0.00008288824],"category_scores_gemma":[0.00006370595,0.000298247,0.0002354985,0.0001939575,0.0002623542,0.0003876505,0.000368777,0.0001756523,0.00009431902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009045975,"about_ca_system_score_gemma":0.00000767704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004964473,"about_ca_topic_score_gemma":0.00007631638,"domain_scores_codex":[0.9974055,0.0001008605,0.0007306449,0.0004244816,0.0007702528,0.0005682652],"domain_scores_gemma":[0.9984382,0.00008736693,0.0007320811,0.000551257,0.00001459709,0.0001765222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005366506,0.0001261003,0.2810703,0.00007325085,0.0000840378,0.000009974419,0.001557219,0.04815453,0.5539703,0.000001362224,0.0002348888,0.1146644],"study_design_scores_gemma":[0.0009873389,0.0001443606,0.6270447,0.0005840223,0.0002017391,0.0003978288,0.0008515858,0.2109058,0.1530179,0.00006144972,0.004807319,0.0009959587],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989394,0.00007772772,0.006697023,0.00005878847,0.0002254155,0.0003322275,0.00001156801,0.00003804921,0.003165218],"genre_scores_gemma":[0.968719,0.00001841215,0.03063758,0.00002780033,0.0001084298,2.033327e-8,0.00001860565,0.00004447453,0.0004256647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4009523,"threshold_uncertainty_score":0.999947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641743065058315,"score_gpt":0.2011654787397813,"score_spread":0.1847480480891981,"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."}}