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 68 · 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 68
Collection-level dates
1948–1977
Open PDF at page 68 ↗

Page transcription

that Andy used for his hybridization techniques would work particularly well
when we were going about setting up an input data for the purposes of classification,
classification as contrasted with the discovery of hybrids. Now it is really
what Andy would do when he tried to discover hybrids is
to try to take pieces of contrasting information from the plant specimens
that he employed. For example, he might discover the characters of a plant
and population vary according to the amount of pubescence
which the plants have, in other words, the amount of hair,
the amount of foliage or some other part. You might discover that some of his plants had
no pubescence whatsoever anywhere, but that as he examines these hybrids
he discovers they were arranged going from none up to a lot of
a real fuzzy plant so to speak. So he discovered that he couldn't really very
precisely say that a plant in this particular area here had any more or less
than some other particular plant, but for his purposes in order to put his
plants into packages maybe he could sort of force a decision about the
pubescence of a particular plant where he had to sort of have a range.
That he could say,"Okay, for my purposes this is the study of hybridization".
I can decide that a group of plants which most people
can be called a one character, or one attribute of a character of pubescence.
That is he looked at these plants and furthermore he discovered that there was
some, and this is a very qualitative terminology. They have an intermediate
amount of pubescence or hair. In other words, it had, they
obviously had more than had none, although some of the ones
that he/ have decided had none had some.
nice and mixed up for you.
Well this, the way biologists get sort of mixed up so we have to be able to do
something At least he could force some sort of decision where he might
decide that this was an attribute where he had some hair and he just labeled
whether one, two, and three or whether or not