{"id":"W4297826356","doi":"10.5194/essd-14-4077-2022","title":"Global datasets of leaf photosynthetic capacity for ecological and earth system research","year":2022,"lang":"en","type":"article","venue":"Earth system science data","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Photosynthesis; Satellite; Photosynthetic capacity; Atmospheric sciences; Environmental science; Mathematics; Botany; Physics; 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":[0.001035956,0.0008283305,0.0005460568,0.002148205,0.0002619994,0.0005562286,0.0005992268,0.0006887528,0.003991914],"category_scores_gemma":[0.002118006,0.0002545141,0.0007743129,0.00636278,0.0001916821,0.0008331256,0.0009898593,0.0006320959,0.00208752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005278704,"about_ca_system_score_gemma":0.000814303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01207845,"about_ca_topic_score_gemma":0.01064209,"domain_scores_codex":[0.9995075,0.0001063259,0.00004745942,0.0001574937,0.0001336484,0.00004757724],"domain_scores_gemma":[0.9986602,0.0001681124,0.0002425533,0.0004755067,0.0003701961,0.00008339161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006477304,0.0004950647,0.341852,0.002837828,0.002709176,0.0002944164,0.0004123018,0.1303658,0.04986385,0.01021802,0.1917042,0.2685996],"study_design_scores_gemma":[0.0002079767,0.0001074207,0.7408047,0.0001933213,0.0003185917,0.0001604929,0.0003187661,0.05545879,0.01392909,0.006249088,0.1821252,0.0001265754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1656249,0.001153627,0.02755253,0.0005485852,0.000120512,0.0001880621,0.7896577,0.004290623,0.01086349],"genre_scores_gemma":[0.3000964,0.0005094881,0.03771964,0.0001836597,0.00004862091,0.0005246594,0.6592743,0.000382101,0.001261109],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01207845,"threshold_uncertainty_score":0.02401632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06897073284752128,"score_gpt":0.2981095917566041,"score_spread":0.2291388589090828,"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."}}