{"id":"W1643008739","doi":"10.5194/bg-12-4509-2015","title":"Reviews and Syntheses: optical sampling of the flux tower footprint","year":2015,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; National Aeronautics and Space Administration; University of Alberta; National Science Foundation","keywords":"Remote sensing; Photochemical Reflectance Index; Context (archaeology); Sampling (signal processing); Footprint; Flux (metallurgy); Extrapolation; Environmental science; Normalized Difference Vegetation Index; Scale (ratio); Vegetation (pathology); Computer science; Leaf area index; Geography; Ecology; Mathematics; Cartography; Telecommunications; Statistics","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":[0.0004820522,0.00004833212,0.00007242772,0.00001069333,0.00006061796,0.00002163383,0.0002091351,0.00002190901,0.00004087342],"category_scores_gemma":[0.0001604536,0.00002538194,0.00002440152,0.0001640711,0.0003944565,0.00005700419,0.0002003685,0.00003296639,0.00001533231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001748746,"about_ca_system_score_gemma":0.000008918018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003272813,"about_ca_topic_score_gemma":0.00008022344,"domain_scores_codex":[0.9994331,0.00002301902,0.0001218366,0.0001329525,0.0001876471,0.000101425],"domain_scores_gemma":[0.9997296,0.00003199332,0.00004587636,0.0001290019,0.000003571246,0.00005993528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008924577,0.0001003354,0.90206,0.00001253244,0.000004569173,0.000001275748,0.001796168,0.001030334,0.02632536,0.004459089,0.0003879985,0.06381346],"study_design_scores_gemma":[0.0003983283,0.0003239712,0.4418863,0.000269759,0.00007811874,0.0001223025,0.001477039,0.03418581,0.01228333,0.01309389,0.4950424,0.000838707],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859357,0.0001769972,0.0004510809,0.0002355026,0.0001413514,0.000109622,0.000004616807,0.00000587151,0.01293923],"genre_scores_gemma":[0.9971845,0.00003646619,0.002443285,0.00003682489,0.00000694219,0.000003352307,3.114444e-7,0.000001248828,0.0002871383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4946544,"threshold_uncertainty_score":0.1453391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05159202578115609,"score_gpt":0.2547995435404065,"score_spread":0.2032075177592504,"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."}}