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:

plant collecting and fieldwork in South America medicinal, culinary, or other traditional uses of plants chromosomes and plant classification early herbals and botanical illustrations botanical institutions exchanging plants, seeds, and specimens microscopy and photomicrography in botanical research How did botanists and gardeners introduce plants from one region to another? grafting, budding, and propagation of tropical fruit crops

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 94 · DO #4708 · 231_Rogers_Bx3FF31_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Graduate Students Garrido, Johnston, McArthur & McCarthy, 1974–1979
Digital Object
DO #4708, page 94
Collection-level dates
1948–1977
Open PDF at page 94 ↗

Page transcription

Potentilla probably derives from the tendency of many species to reproduce apomictically, with interspecific hybridization comparatively frequent, and high chromosome numbers that may vary widely within a population.

It is proposed to estimate the components of variance within each of a number of populations in sect. Multijugae, for each of a large number of characters. These, together with estimates of correlation between pairs of characters, will be used to indicate which characters might be useful in the later classification phase of this study. Between populations, a large set of herbarium specimens (including "exemplars" of the sampled populations) will then be subjected to character analysis, which will lead to further refinement in the set of possible characters for the classification.

A large set of herbarium specimens will then be classified, using the refined characters deduced from the inter- and intra-population studies above, and using a technique which allows comparison of a large set of characters in calculating a similarity measure.

Potential results will include a revision of sect. Multijugae, showing in what way(s) past classification schemes are correct, and in what way(s) they are incorrect; and an evaluation of the proposed method, that is, the use of population samples as an aid in classification using a clustering procedure. Such work may also point the way to solution of the classification problems in other sections of the genus.