URM - Herbário Pe. Camille Torrand
Bibliographic record
Abstract
O Herbário Pe. Camille Torrend (URM) foi criado em 1954, sob a chefia do ilustre micologista Prof. Augusto Chaves Batista. No seu primeiro ano de funcionamento, o Herbário URM tinha 1141 exsicatas registradas. Cedo esse número foi aumentado, graças às doações feitas, entre outros, pelo Prof. Petrak (fungos da Áustria), pelas Universidades de Pavia e Universidade da Califórnia e pelo Instituto de Botânica da Universidade de Turku, Finlândia (Pucciniaceae). Entre os principais coletores nacionais, destacaram-se Bento Pickel, E.P. Heringer, Oswaldo Soares, Dárdano de Andrade Lima, Albino Vital e o próprio Prof. Augusto Chaves Batista. Dentre os importantes coletores internacionais, há de se ressaltar a colaboração dos seguintes cientistas: Pe. Camille Torrend, E. Petrak, Rolf Singer, J.L. Howard, G.W. Martin, T.H. MacBride, N. Harrington, J.P. Trace, J.A. von Arx, B. Cooke, R. Ciferri, K. Pirozinski, H. Johannes, e outros. Das principais coleções do Herbário URM, constam ainda duplicatas da Flora Santesson, Fungi Excicattii Fennici, Mycologicum Herbarum - Washington State College, Fungi of Oregon, Flora da Suécia, Herbarum Museu Botanica Upsaliensis, Fungi Bohemia, University of Toronto e Herbarium - Pennsilvania State College, entre outras. Atualmente, o Herbário URM possui mais de 90.000 registros, e a sua coleção de fungos herborizados é uma das maiores da América Latina, constando principalmente de amostras das regiões Norte e Nordeste do Brasil.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.094 | 0.029 |
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".