{"id":"W4292383652","doi":"10.1039/d1en01078f","title":"Are nanomaterials leading to more efficient agriculture? Outputs from 2009 to 2022 research metadata analysis","year":2022,"lang":"en","type":"article","venue":"Environmental Science Nano","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Metadata; Agriculture; Computer science; Data science; Information retrieval; World Wide Web; Geography; Archaeology","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009212799,0.0009749327,0.0008207432,0.03781907,0.0007257682,0.004406924,0.0005949532,0.0005745422,0.01420158],"category_scores_gemma":[0.0209132,0.00028321,0.001293179,0.06405924,0.0004015021,0.00310689,0.00224317,0.0005291016,0.008991966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002888427,"about_ca_system_score_gemma":0.008065546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01295707,"about_ca_topic_score_gemma":0.01278357,"domain_scores_codex":[0.9928603,0.001031065,0.001637092,0.0007681933,0.003157219,0.0005461173],"domain_scores_gemma":[0.9680266,0.01060952,0.005332441,0.001403266,0.01346205,0.001166095],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00179525,0.0001083489,0.07752416,0.03511998,0.0007909032,0.0006391063,0.002600783,0.003136459,0.01638827,0.01737707,0.26349,0.5810297],"study_design_scores_gemma":[0.00004447525,0.0001689431,0.1691303,0.006356976,0.0007426669,0.0002898664,0.00394561,0.0008571018,0.01192473,0.003061769,0.8033684,0.0001090611],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.04320502,0.01836915,0.003321881,0.003947727,0.0006116476,0.0001855486,0.8611926,0.001291646,0.06787477],"genre_scores_gemma":[0.1620226,0.05243993,0.01716,0.001048145,0.0006011311,0.0006189322,0.738285,0.0008352729,0.02698896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9907872,"threshold_uncertainty_score":0.04872257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414968511605382,"score_gpt":0.3044995576039548,"score_spread":0.270349872487901,"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."}}