O Jardim das Amoreiras e o resgate da memoria de Seropédica/RJ no fio da seda: um projeto de extensão no Jardim Botânico da UFRRJ
Bibliographic record
Abstract
The importance of recovering the memory of places and their legacies is contributing to the (re)signification of society's identity and its valuation. The “Mulberry Garden” is an extension project developed with the objective of recovering a little part of the history of the Seropédica municipality, whose name derives from sericulture (the cultivation of the silkworm), an activity developed there in the middle of the 19th century. The arboretum, planted in the Botanical Garden located on the campus of the Federal Rural University of Rio de Janeiro, intends to be a space to rescue a bit of local history and explain what sericulture is, through a series of objects and actions, as an educational guided trail, open to the community. The project is developed in partnership between the Departments of Geography, Animal Production and Botany and UFRRJ Technical School. The UFRRJ's Botanical Garden was chosen because it is a unit that performs research, teaching, extension, conservation, and enhancement activities for botanical species; with multi and interdisciplinary perspectives, helping to rescue the memory of the municipality through planting mulberry trees (Morus alba L., Moraceae), an exotic species whose leaves are the food of the silkworm (Bombyx mori L., 1758; Lepidoptera: Bombycidae). The “Mulberries Garden” occupies an area of 70m2, consisting of three alleys of mulberry trees delimited by a lawn whose boundaries reproduce, in scale, the territoriality of Seropédica. In addition, the Project aims to develop teaching materials and promote activities with schools, the local community, and the university community. Through visitation, observation and experimentation of materials, a bit of the city's history is recovered, delving into its past (in geological, biological, and historical times) until the present day. In another way, the creation of the “silkworm” and the production of silk thread are also rescued and publicized, as sustainable activities that can unfold into other ventures.
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.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".