Agriculture and pesticide regulation in Latin America: A comparative dataset with the European Union
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dataset listing the active ingredients approved as of 31 December 2020 in eight Latin American countries evaluated for 10 major crops, together with their legal status in the European Union, chemical identifiers (CAS number, IUPAC name), and primary target organism. The dataset was generated collaboratively by 20 members of the Sociedad Latinoamericana de Investigación en Abejas (SOLATINA, https://solatina.org/) as part of the working group Impacto antrópico. It includes five tables as CSV files covering complementary aspects of agricultural production and pesticide regulation. Table S1 provides national-level agricultural context, including mean land and cultivated areas (2015–2019), the percentage of cultivated land, agricultural contribution to GDP, and each country’s share of total cultivated land and primary production in Latin America. Table S2 compiles crop-level information such as harvested area in 2019, gross production and export values (2016–2020), and each crop’s relative importance within national agricultural systems. Table S3 lists pesticide active ingredients approved in each country, with details on their common and IUPAC names, CAS numbers, EU approval status under Directive EC 1107/2009, expiry information, WHO classification, source, category, and crop-specific registrations. Table S4 compiles active ingredients banned in the region, indicating inclusion in international conventions (Montreal, Rotterdam, Stockholm), WHO classification, EU regulatory status, and national bans by country. Finally, Table S5 contains aggregated variables used for generalized linear mixed models (GLMMs), including country, crop, number of pesticides, approval status in the EU, pollination mode, crop presence in the EU, mean export value, and mean production. It also includes three TXT files as support for plotting. This dataset enables cross-regional analyses of agricultural production and pesticide regulation, highlighting regulatory alignment and disparities between Latin America and the European Union. The repository also includes the R scripts used to analyze all the data and generate all figures asociated to a scientific publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it