{"id":"W2140440859","doi":"10.1890/14-0005.1","title":"Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy‐scale photosynthesis","year":2015,"lang":"en","type":"article","venue":"Ecological Applications","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Université Laval; Université de Montréal","funders":"Natural Resources Canada; U.S. Department of Agriculture; Natural Sciences and Engineering Research Council of Canada; National Park Service; U.S. Forest Service; U.S. Geological Survey; Biological and Environmental Research; Canadian Foundation for Climate and Atmospheric Sciences; Northern Research Station; Northeastern States Research Cooperative; Office of Science; U.S. Department of Energy; National Science Foundation","keywords":"Deciduous; Evergreen; Canopy; Phenology; Environmental science; Eddy covariance; Grassland; Evergreen forest; Understory; Grassland ecosystem; Seasonality; Ecology; Atmospheric sciences; Ecosystem; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000271464,0.0002571971,0.0001955082,0.0007784661,0.00008032242,0.0003817074,0.0001456183,0.0002348092,0.0006589323],"category_scores_gemma":[0.0007948395,0.0001542842,0.0001904451,0.0005245428,0.00009471445,0.0004739451,0.000162728,0.0002068088,0.0002350986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002533051,"about_ca_system_score_gemma":0.0001109295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003327285,"about_ca_topic_score_gemma":0.007552465,"domain_scores_codex":[0.9999187,0.000009237857,0.000004721792,0.00003219008,0.00002241934,0.00001273097],"domain_scores_gemma":[0.999605,0.000127001,0.000151027,0.00003302678,0.00006107117,0.0000228654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000103557,0.00007286118,0.8635995,0.00008115349,0.0001223113,0.00004024606,0.00009796338,0.008817395,0.07107878,0.000148581,0.0003905383,0.05544703],"study_design_scores_gemma":[0.000002875191,0.00002654459,0.9822405,0.000004382057,0.00001759342,0.00005820575,0.00002920459,0.01349414,0.003833183,0.00006788665,0.0002196737,0.000005914065],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895906,0.0001356392,0.008891297,0.000006951011,0.000004018534,0.00001233624,0.0003838246,0.0000882419,0.0008871286],"genre_scores_gemma":[0.991907,0.00009446463,0.006915888,0.000009413849,0.000003188385,0.00001305319,0.0007059609,0.00001467746,0.0003364641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003327285,"threshold_uncertainty_score":0.006615818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107928615008888,"score_gpt":0.2012504936838348,"score_spread":0.190457632182946,"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."}}