{"id":"W4300466282","doi":"","title":"Using virtual weather data to estimate leaf wetness duration","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut de Recherche et de Développement en Agroenvironnement","funders":"","keywords":"Duration (music); Leaf wetness; Computer science; Environmental science; Remote sensing; Meteorology; Geology; Geography; Acoustics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005783023,0.0003064099,0.0003159056,0.00003278759,0.000385981,0.0005477943,0.001940139,0.0002325931,0.0002206978],"category_scores_gemma":[0.001168557,0.0001536025,0.00009077707,0.0002964438,0.00009267229,0.0002150931,0.003131856,0.0002871024,0.00008001406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001460157,"about_ca_system_score_gemma":0.0001259558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003273557,"about_ca_topic_score_gemma":0.007049085,"domain_scores_codex":[0.9953904,0.002282036,0.000476186,0.0009126807,0.0005819592,0.0003567674],"domain_scores_gemma":[0.9963679,0.0002584475,0.0003136482,0.001111427,0.001686643,0.0002619475],"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":[0.000138619,0.001527494,0.005880941,0.0001836571,0.0002158926,0.00001340504,0.01018773,0.0018473,0.405822,0.01188056,0.01482765,0.5474747],"study_design_scores_gemma":[0.00185619,0.0000158093,0.03422651,0.007946236,0.0004522678,0.00005523671,0.00199695,0.3108568,0.1580576,0.008643926,0.4710923,0.00480031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9468333,0.0007361129,0.01226766,0.02871565,0.0005369434,0.0009149205,0.0003307175,0.0002729568,0.009391741],"genre_scores_gemma":[0.9790012,0.00006790453,0.01417714,0.0002158255,0.0001387733,0.00004129504,0.001693838,0.000009194396,0.004654883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5426745,"threshold_uncertainty_score":0.6263726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1191780183469338,"score_gpt":0.2884267518378366,"score_spread":0.1692487334909029,"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."}}