Hunt Institute for Botanical Documentation
A Research Division of Carnegie Mellon University

Hunt Institute Archives Text Discovery Platform

Search a large and growing portion of our online collections, including handwritten documents.
PROTOTYPE

This prototype uses state-of-the-art artificial intelligence, including a vision-language model (VLM) capable of reading handwritten documents as well as typed and printed text, to create searchable transcriptions of digitized materials in the Hunt Institute Archives. This makes it possible to search the textual contents of individual pages, including material that may not be described in the archival catalog.

Use Keyword search for specific words, names, dates, scientific names, or phrases. Try Semantic search (experimental) to describe a topic, question, or kind of material when you do not know the exact wording used in the documents.

About the AI-generated transcriptions

The transcriptions are generated automatically from page images and may contain errors, especially with difficult handwriting, unusual names, multiple languages, image-quality problems, or complex layouts. They are intended primarily as a discovery aid rather than authoritative transcriptions.

Each result provides the generated transcription and links to the original digitized material and associated archival description so that readings can be checked against the source. The transcription workflow uses AI models run locally by the Hunt Institute.

About Keyword and Semantic search

Keyword search is the default and matches the wording in the transcriptions. Results contain all your terms. Use quotes for an exact phrase. Substring matching is supported, so aceae can find plant-family names ending in -aceae.

Semantic search (experimental) is useful when you know what kind of material you are looking for but do not know the words used in the documents. It ranks transcribed passages by similarity of meaning, so relevant results may not contain the exact words in your query.

Semantic queries can be broad research topics, descriptions of activities or relationships, or natural-language questions. For example:

Semantic search is not a chatbot: a question is used as a search query, and the system returns archival passages that appear conceptually related to it rather than generating an answer or summary. Short descriptions and ordinary research questions generally work better than lists of disconnected keywords. Quotation marks have no special meaning in Semantic mode. Cross-language matching may work in some cases, but it should not be treated as translation.

Keyword and Semantic search are complementary. Keyword search lets you require particular wording; Semantic search can surface differently worded passages about the same subject. Depending on the research question, trying both can reveal different useful material.

Open a result: use the prominent page-and-transcription link to see the metadata, PDF, and full transcription. Keyword-search terms are highlighted in the transcription.

Archives Collections Database (ArchivesSpace): the Collection, Item/Folder, and Digital Object links open the corresponding archival records. Collection-level dates describe the collection as a whole, not necessarily the specific item or page.

If a PDF does not load: on the detail page, use the Digital Object link, click “Go to file” in ArchivesSpace, and navigate to the page number shown here.

Current limitations
  • Automated transcriptions can contain missing or incorrect text or unintended repetition. Difficult handwriting, image quality, unusual layouts, and multiple languages can reduce accuracy. Always consult the original page image when an exact reading matters.
  • Semantic search remains experimental. Its rankings are an additional discovery aid, not a complete or definitive set of relevant results, and highly ranked pages can sometimes be only broadly related.
  • This is an active prototype. Search coverage, transcriptions, functionality, and the interface may continue to change as additional archival material is processed and the system is improved.

← Back to results

Page 63 · DO #4808 · 231_Rogers_Bx6FF29_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Prance, White & Bisby, 1968–1970
Digital Object
DO #4808, page 63
Collection-level dates
1948–1977
Open PDF at page 63 ↗

Page transcription

TAXONOMIC HISTORY

The Chrysobalanaceae is a woody group almost confined to the tropics. Although it was given family rank by Robert Brown as long ago as 1813, all the authors of the best-known and widely used systems of classification (De Candolle, Bentham and Hooker, Engler and Prantl and John Hutchinson) have treated it as a tribe or subfamily of Rosaceae. Nevertheless, nearly all workers with specialist knowledge of the group, e.g. Fritsch (1888) and particularly those who have studied its anatomy, e.g. Hallier (1903, using the results of the comprehensive study on leaf-anatomy by Kuster (1897)), Juel (1915, ovary-structure) and Bonne (1928, floral anatomy) have considered it to be sufficiently different from Rosaceae to be treated as a separate family. Both Metcalfe and Chalk (1950) and Erdtman (1952) imply that no objections could be raised against treating the group as a family on the basis of anatomy and pollen-grain structure respectively.

The most recent comprehensive treatment at generic level is that of Focke (1891) in Engler and Prantl's 'Die natürlichen Pflanzenfamilien'. The most recent world-wide treatment at specific level (De Candolle, 'Prodromus', 1825) was written at a time when less than ten-per-cent of species now known had been described.

Focke included the following genera:
Chrysobalanus, Grangeria, Moquilea, Licania, Hirtella, Couepia, Parinari, Acioa, Angelesia, Parastemon, Lecostemon ('Lecostomion') and Stylobasium. The last two were included with some reservations and he suggested a relationship for them with Phytolaccaceae. Since Focke's time three additional genera have been described - Geobalanus, Magnistipula and Afrolicania.

In the first phase of our own work we assembled comprehensive taxonomic data based on morphology, anatomy, palynology and blastogeny for as many species as possible, with a view to deciding:

1) whether the group should be given family rank or treated as a subfamily of Rosaceae.