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 6 · DO #964 · 319_Love_Bx1FF9r

Collection
Áskell Löve (1916–1994) papers
Item/Folder
"Proposals", 1959–1972, n.d.
Digital Object
DO #964, page 6
Collection-level dates
1950–1987
Open PDF at page 6 ↗

Page transcription

I. Background

Since the biosystematic approach to plant taxonomy has grown into an important venture in recent decades, the need for some aid in retrieval of pertinent information has long been pressing. This has been clearly recognized, especially by the large membership of the International Organization of Plant Biosystematists, which already in 1960 made information retrieval one of its main goals. The matter has been discussed at every meeting of this organization, but not until the Botanical Congress in Seattle in early September, 1969 was there any new progress available to demonstrate in what way this might be achieved. At that meeting, the Co-Principal Investigators presented their first attempts at computerizing available chromosome information in a data bank; although the results were only preliminary, they met with enormous enthusiasm and the venture was strongly supported and endorsed by the IOPB membership and executives.

Although biosystematic data are of various kinds, the majority of them at present are directly connected with chromosome studies and, especially, with determinations of chromosome numbers. The first chromosome numbers were counted in 1882 by the French botanist Guignard and the German botanist Strasburger, but it took more than a generation before the taxonomical importance of such studies became generally recognized. Not until around 1940 did the significance of this approach become generally acknowledged as the strongest basis for evolutionary classification, with the result that the number of published chromosome numbers has grown from about 500 in 1910 to well above 7,000 in 1969. Although only about 40,000-45,000 species of higher plants and around 10,000 species of lower plants have been studied from this point of view, the published items are well over 400,000, in more than 20,000 different publications. These numbers indicate the magnitude of the problem, because all other biosystematic publications together are estimated to reach less than ten percent of this figure. It ought to be mentioned that although chromosome numbers are important for solving many taxonomic and evolutionary problems, they are also of great value for studies of dispersal and distribution, and for all phases of practical and theoretical cytogenetics, and that the number, morphology, and size of chromosomes are known to be significant for the