{"id":"W2914308230","doi":"10.3390/rs11030273","title":"Diurnal and Seasonal Solar Induced Chlorophyll Fluorescence and Photosynthesis in a Boreal Scots Pine Canopy","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts","funders":"Academy of Finland; Natural Environment Research Council; Sight Research UK","keywords":"Environmental science; Eddy covariance; Atmospheric sciences; Canopy; Primary production; Radiance; Photosynthetically active radiation; Scots pine; Remote sensing; Leaf area index; Chlorophyll fluorescence; Photochemical Reflectance Index; Solar zenith angle; Normalized Difference Vegetation Index; Chlorophyll; Photosynthesis; Ecology; Geography; Physics; Botany; Ecosystem","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001200196,0.0001355247,0.0001306759,0.0002859102,0.0002155425,0.0001838505,0.0001046262,0.0001349499,0.0002095594],"category_scores_gemma":[0.0001450699,0.00009318093,0.0001320178,0.0002193728,0.0001102046,0.0001339713,0.0000824043,0.0001107758,0.00004969629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002698255,"about_ca_system_score_gemma":0.0001401606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01566398,"about_ca_topic_score_gemma":0.0214883,"domain_scores_codex":[0.9999576,0.000003838668,0.000002341237,0.00001784269,0.00001055117,0.000007759412],"domain_scores_gemma":[0.9999095,0.00001598499,0.00002632651,0.000006145004,0.00001781878,0.0000242294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006193009,0.0001973721,0.6282421,0.00005428293,0.0000647679,0.0004759227,0.0003781453,0.001799041,0.3553116,0.00008818765,0.0002248873,0.01254443],"study_design_scores_gemma":[0.000001977476,0.00003518325,0.9976622,8.364086e-7,0.000003884881,0.00006825292,0.00003059078,0.0008554808,0.001271505,0.000009372877,0.000057991,0.000002806476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995335,0.00004874566,0.000111066,0.000002607044,0.000001254768,0.000002171741,0.0001158077,0.000008016015,0.0001768082],"genre_scores_gemma":[0.9995766,0.00001788217,0.000163047,0.000003609014,0.000001403947,0.000002684402,0.00015421,0.000001493965,0.00007914928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01566398,"threshold_uncertainty_score":0.03114563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006210538010233653,"score_gpt":0.1975216168887286,"score_spread":0.191311078878495,"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."}}