{"id":"W2964498920","doi":"10.5539/jas.v11n14p198","title":"Determination of the Leaflet Area of Schinus terebinthifolius Raddi in Function of Linear Dimensions","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Mathematics; Mean squared error; Linear regression; Coefficient of determination; BETA (programming language); Power function; Geometric mean; Statistics; Function (biology); Correlation coefficient; Square (algebra); Regression analysis; Combinatorics; Horticulture; Botany; Biology; Geometry; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007448525,0.00007712247,0.0002060913,0.00002882557,0.0000740636,0.00001123915,0.0004159918,0.00004048983,0.00005342791],"category_scores_gemma":[0.0001608255,0.00001879187,0.0001276375,0.0007896149,0.0001606657,0.0003269551,0.00008371587,0.0001277278,0.000001393555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004811137,"about_ca_system_score_gemma":0.00003231753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001186868,"about_ca_topic_score_gemma":0.00009656963,"domain_scores_codex":[0.9985277,0.00005965988,0.0004930259,0.0001088767,0.0006685373,0.0001421982],"domain_scores_gemma":[0.9986691,0.00006124409,0.0006456499,0.00005475806,0.000523797,0.00004542537],"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":[0.00003319915,0.00007103442,0.02090947,0.000009255573,0.000002928613,1.824753e-7,0.0001561031,0.0001142401,0.9712646,0.00004537349,0.00003112366,0.007362513],"study_design_scores_gemma":[0.0001621516,0.0005394402,0.8556754,0.0001599728,0.00001095563,0.0000117121,0.0006321836,0.00006439866,0.1425158,0.00005600732,0.0001213586,0.00005063576],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998634,0.0001122948,0.000001838685,0.0005997577,0.0002913626,0.0001375485,0.000003463747,0.000001626228,0.0002180867],"genre_scores_gemma":[0.9997253,0.00001886431,0.0001148786,0.00002449969,0.00004220216,5.005911e-7,4.66808e-7,2.837419e-7,0.000073048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8347659,"threshold_uncertainty_score":0.07730233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215103655144065,"score_gpt":0.2071649766178614,"score_spread":0.1850139400664208,"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."}}