{"id":"W2261659740","doi":"10.1093/aob/mci264","title":"Specific Leaf Area and Dry Matter Content Estimate Thickness in Laminar Leaves","year":2005,"lang":"en","type":"article","venue":"Annals of Botany","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":497,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fondo para la Investigación Científica y Tecnológica; Department for Environment, Food and Rural Affairs, UK Government","keywords":"Specific leaf area; Biology; Dry matter; Leaf size; Dry weight; Botany; Covariance; Agronomy; Horticulture; Statistics; Mathematics; Photosynthesis","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":[],"consensus_categories":[],"category_scores_codex":[0.0002779198,0.0001007136,0.0001678413,0.000009464295,0.00005254585,0.00003159412,0.0001097054,0.00004906079,0.0004398269],"category_scores_gemma":[0.00001176085,0.0000368934,0.00004342688,0.00007944582,0.0000729773,0.0001211703,0.00004721203,0.00006949405,0.00006187465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006345802,"about_ca_system_score_gemma":0.000002177081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001762852,"about_ca_topic_score_gemma":0.0003471348,"domain_scores_codex":[0.9991878,0.00003606261,0.0002190788,0.0001838874,0.0001572442,0.0002159651],"domain_scores_gemma":[0.9997401,0.00003511261,0.00005898636,0.00005008494,0.00006390552,0.000051787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000163951,0.0002750825,0.2799436,0.0000344111,0.00001628191,0.0000104294,0.0004141257,0.00001091984,0.5398782,0.0001918466,0.008585718,0.1704754],"study_design_scores_gemma":[0.0001493149,0.0001584589,0.9384348,0.0000846016,0.000002802039,0.00000353362,0.0003145129,0.00003881356,0.03246029,0.0001961485,0.02801047,0.0001462802],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787633,0.002829878,0.000001304379,0.01661305,0.00003259075,0.0001064197,0.000007935088,0.0000106538,0.001634795],"genre_scores_gemma":[0.9973933,0.0004487923,0.00008437633,0.001160644,0.0000803199,0.000006011252,0.000001502873,8.632998e-7,0.0008241933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6584911,"threshold_uncertainty_score":0.4815797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1346909355206042,"score_gpt":0.2647484299383431,"score_spread":0.130057494417739,"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."}}