{"id":"W2103388666","doi":"10.1065/espr2006.01.002","title":"Chemical Partitioning to Foliage: The Contribution and Legacy of Davide Calamari","year":2005,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Environmental science; Fugacity; Boundary layer; Environmental chemistry; Penetration (warfare); Atmosphere (unit); Cuticle (hair); Biological system; Soil science; Chemistry; Biology; Meteorology; Mathematics; Geography; Physics; Operations research","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":[0.001258621,0.0003345576,0.0005102737,0.001100368,0.001839588,0.0020283,0.0006720508,0.0009086091,0.002442901],"category_scores_gemma":[0.002234224,0.000316621,0.0001603205,0.0009937643,0.002184174,0.001433223,0.001289362,0.001502718,0.0006383023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002049421,"about_ca_system_score_gemma":0.001308482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05363375,"about_ca_topic_score_gemma":0.07112987,"domain_scores_codex":[0.9995753,0.00005467726,0.000008849154,0.0001758349,0.0001274064,0.00005792693],"domain_scores_gemma":[0.9988881,0.0003504524,0.00008657738,0.0001049992,0.0004251186,0.0001448461],"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":[0.0008332137,0.0002140679,0.06152305,0.001033011,0.0001660912,0.001322907,0.0054023,0.001953379,0.04582539,0.07501034,0.08984457,0.7168717],"study_design_scores_gemma":[0.00002029562,0.0001097602,0.08544693,0.000211168,0.00007312037,0.001260397,0.00131715,0.001491154,0.01010271,0.01382656,0.8860503,0.000090375],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"commentary","genre_scores_codex":[0.4900688,0.2659153,0.01001497,0.1146713,0.003149148,0.00006157071,0.0009470537,0.0002169106,0.114955],"genre_scores_gemma":[0.7485319,0.1419567,0.007881672,0.01418368,0.002289744,0.00002517832,0.0002938423,0.0002239333,0.08461334],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.05363375,"threshold_uncertainty_score":0.1066431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0134201914802473,"score_gpt":0.2801272368135728,"score_spread":0.2667070453333255,"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."}}