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 72 · DO #4666 · 231_Rogers_Bx2FF27_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Taximetrics Course - Student Reports, 1966–1968
Digital Object
DO #4666, page 72
Collection-level dates
1948–1977
Open PDF at page 72 ↗

Page transcription

His connection with plants therefore was with a different set of plant materials than he would have found occurring in the temperate parts of Europe. So either Linnaeus' or Tournefort's systems of classification sort of fell flat on their face as when he wanted to make a classification of the plants that he found in the tropics. So when he discovered his that his classification system had to be something different, I can't imagine what background he had, it would be interesting as a historical study to know how Adanson was backed up with ideas from other people. We don't have that available to us now, so we just have to assume that he started from scratch with no other ideas behind him. But basically the idea that Adanson had was that any piece of information that you can get from the plant could be used or might be useful in the classification thereof. It didn't matter whether you were looking at the root, stem, leaf, or some part of the reproductive structures, that if there were pieces of information available from them, you should attempt to use that information in making a classification. What he did for himself was to take one piece of information or one character. For example he might of taken the same sorts of characters that Anderson used here, or let us say that he was interested in pubescence. He would try to make a classification of his plants purely on the basis of that character, one character. Then he would take another character of the plant and it might be something about the flowers. Let us say he had a differentiation on the number of stamens in the flower. He would make a classification based upon the stamens, differentiations that he could find. This would be a separate classification from the one that he made on the basis of pubescence. And he would go through and make classifications based on several characters simultaneously. Then he would go back and try to put these classifications together and see where he got the most content of information. Now this is an ideal idea and, of course, you can see immediately how this is going to get you into