{"id":"W4225164731","doi":"10.5194/essd-2022-136","title":"Global Datasets of Leaf Photosynthetic Capacity for Ecological and Earth System Research","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":9,"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; Photosynthetic capacity; Earth observation; Satellite; Mathematics; Environmental science; Atmospheric sciences; Geography; Botany; Biology; Physics","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":[0.0009541638,0.0007870173,0.0005265228,0.002116922,0.0002388772,0.000540542,0.0005714333,0.0006748604,0.003608834],"category_scores_gemma":[0.002117295,0.00022941,0.0007603872,0.005843888,0.0001849933,0.0007972984,0.0009407623,0.0005516034,0.001925969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005086784,"about_ca_system_score_gemma":0.0007158008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01036617,"about_ca_topic_score_gemma":0.009816368,"domain_scores_codex":[0.9995266,0.0001063069,0.00004314566,0.000158032,0.0001252766,0.00004066611],"domain_scores_gemma":[0.9987342,0.0001565169,0.0002239463,0.0004663761,0.0003517314,0.00006733043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006499931,0.000502352,0.2903244,0.002827788,0.002644877,0.000293672,0.0003902294,0.1443781,0.0597547,0.01089016,0.1567135,0.3306304],"study_design_scores_gemma":[0.0002083661,0.0001263135,0.7098339,0.0002140836,0.0003733129,0.0001881582,0.0003496221,0.09140379,0.01841243,0.008235249,0.1705087,0.0001460759],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.212461,0.001496044,0.04917783,0.000643887,0.0001634579,0.0002018208,0.7172809,0.005878976,0.01269614],"genre_scores_gemma":[0.3802582,0.0005916704,0.05213606,0.000177753,0.00005959536,0.0004961652,0.5644938,0.0004346234,0.001352081],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01036617,"threshold_uncertainty_score":0.02061164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05203315016055091,"score_gpt":0.2966047873093252,"score_spread":0.2445716371487743,"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."}}