REGRAS PARA NOMENCLATURA DOS NOMES COMUNS DOS AGROTÓXICOS
Bibliographic record
Abstract
Este trabalho visou subsidiar as ações da Agência Nacional de Vigilância Sanitária (ANVISA) na padronização da nomenclatura dos ingredientes ativos usados em agrotóxicos, segundo normas internacionais. As regras apresentadas resultam do trabalho de revisão da ortografia dos nomes comuns dos agrotóxicos em uso no Brasil, publicados na Relação de monografias dos ingredientes ativos de agrotóxicos, domissanitários e preservantes de madeira da ANVISA (2002), conforme Resolução No 347/02. Foram utilizados os princípios gerais de atribuição de nomes comuns publicados pela International Standards Organization (ISO) e as regras de nomenclatura de compostos orgânicos. O artigo não pretende esgotar a discussão sobre esse tema, sugerindo a padronização da ortografia dos nomes comuns dos agrotóxicos para facilitar o uso da informação pelos técnicos das áreas acadêmica, comercial e governamental. RULES FOR NOMENCLATURE OF PESTICIDES COMMON NAMES Abstract This work sought to subsidize the actions of the Agência Nacional de Vigilância Sanitária (ANVISA) in the standardization of the nomenclature of the active ingredients used in pesticides, according to international norms. The presented rules result of the work of revision of the spelling of the common names of the pesticides in use in Brazil, published in the Relação de monografias dos ingredientes ativos de agrotóxicos, domissanitários e preservantes de madeira of ANVISA (2002), according to Resolution N. 347/02.The general principles of attribution of common names published by International Standards Organization (ISO) and the rules of nomenclature of organic compounds were used. The article doesnt intend to drain the discussion on that theme, suggesting the standardization of the spelling of the common names of the pesticides to facilitate the use of the information for the technicians of the academic, commercial and government areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".