{"id":"W2149550872","doi":"10.1016/j.agrformet.2014.01.016","title":"Continuous observation of leaf area index at Fluxnet-Canada sites","year":2014,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"FluxNet; Leaf area index; Environmental science; Remote sensing; Atmospheric sciences; Vegetation (pathology); Meteorology; Eddy covariance; Ecosystem; Geography; Ecology; Geology","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":[0.00009360987,0.0001572434,0.0002421144,0.000009384713,0.0001058586,0.000009569191,0.0001272414,0.0001214502,0.0001263343],"category_scores_gemma":[0.00007384787,0.00008535582,0.000037897,0.0001453069,0.0001734041,0.0000947713,0.0001463043,0.00009562674,0.00001990451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007578242,"about_ca_system_score_gemma":0.000004139416,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05662416,"about_ca_topic_score_gemma":0.5134001,"domain_scores_codex":[0.999015,0.00006937292,0.000215343,0.0002648569,0.000193247,0.0002422185],"domain_scores_gemma":[0.9995055,0.0001154233,0.0001481535,0.0001235927,0.00002462382,0.00008267092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002210337,0.00001641182,0.8911787,0.000007465923,0.00001763617,0.000002332153,0.0001039917,0.001367087,0.08397645,0.0001996816,0.02073404,0.002374157],"study_design_scores_gemma":[0.0002168714,0.0001142291,0.9875068,0.000004165529,0.00001831442,0.00006163321,0.00004264363,0.0007767527,0.001915326,0.0002652663,0.008939009,0.0001389998],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950505,0.00004354668,0.00002136621,0.0007536958,0.0001232123,0.000124941,0.000003529795,0.00001994397,0.003859295],"genre_scores_gemma":[0.9973159,0.0000110936,0.0004079865,0.0003289488,0.00005264398,0.000001588055,0.00006124603,0.000004312144,0.001816269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4567759,"threshold_uncertainty_score":0.9496579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006836708164482491,"score_gpt":0.1662140637165084,"score_spread":0.1593773555520259,"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."}}