Herbarium Areqvipense (HUSA): informatización y representatividad de su colección
Bibliographic record
Abstract
Scientific collections and herbaria are essential sources of information and education for researchers and practitioners in biological sciences. The Herbarium Areqvipense (HUSA), registered at Index Herbariorum since 2004, holds one of the most important collections in Peru. In this paper we provide information about the collection, and its representativeness for the Peruvian flora. HUSA has more than 11000 specimens recorded to date, with more than 2300 determined species, consisting mostly of Magnoliophyta and Pteridophyta (ca. 98%), and a smaller proportion of Basidiomycetes, Ascomycetes (fungi and lichens) and Bryophyta (mosses). The collection includes specimens from 23 departments of Peru, where the samples belonging to Arequipa have the largest number of individuals collected (3375) accounting for 31% of the collection. Asteraceae and Solanaceae are the most collected with 1571 and 964 specimens, respectively. The majority of geo-referenced specimens came from the tropical wet forest with 15%, followed by the tropical pre-montane wet forest with 8%. We also provide a list of the nomenclatural types and a brief summary of the history and development of HUSA since its creation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.016 |
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".