{"id":"W2904505420","doi":"10.1515/intag-2017-0048","title":"Ancillary vegetation measurements at ICOS ecosystem stations","year":2018,"lang":"en","type":"article","venue":"International Agrophysics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; National Science Foundation","keywords":"Environmental science; Biomass (ecology); Eddy covariance; Ecosystem; Greenhouse gas; Vegetation (pathology); Enhanced vegetation index; Leaf area index; Remote sensing; Ecology; Vegetation Index; Normalized Difference Vegetation Index; Geography","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007138464,0.0001227112,0.00007760113,0.000006012859,0.0001658497,0.00002295265,0.0002325444,0.00003757067,0.001123479],"category_scores_gemma":[0.00001037125,0.0001192254,0.000050532,0.00009982863,0.00012673,0.0002707573,0.0001987803,0.00005187334,0.003548122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008135358,"about_ca_system_score_gemma":0.000005645864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006606871,"about_ca_topic_score_gemma":0.0001525111,"domain_scores_codex":[0.9988011,0.00002289623,0.0001839835,0.0002505133,0.0005740839,0.0001674702],"domain_scores_gemma":[0.999637,0.00001472201,0.0001029085,0.0001554758,0.00002102121,0.00006885697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001733026,0.0008989798,0.5307958,0.00002591539,0.0003330523,0.00002666833,0.002651154,0.226014,0.148298,0.002230455,0.008009946,0.08054283],"study_design_scores_gemma":[0.001301833,0.0003297391,0.7294189,0.00005103827,0.00006255413,0.00003221527,0.0001707693,0.1740164,0.009931281,0.005416803,0.07840101,0.0008675214],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436622,0.000005935733,0.02207859,0.0001395436,0.0006257407,0.0001276773,0.000008442494,0.00004408047,0.03330784],"genre_scores_gemma":[0.9899411,0.000007875436,0.007200881,0.0003463434,0.000218307,0.00001654115,0.00003217177,0.00001744449,0.002219357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1986231,"threshold_uncertainty_score":0.9997897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01355274542512231,"score_gpt":0.2200289349071996,"score_spread":0.2064761894820773,"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."}}