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 84 · 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 84
Collection-level dates
1948–1977
Open PDF at page 84 ↗

Page transcription

6. Pubescence: whether strigose, sericeous, tomentose, glandular, etc.; relative thickness and distribution. Used for divisions of the genus by Wolf, which has been criticized by Clausen at al. (1940); as a secondary character by Rydberg. It probably is less important than either of these workers thought. Many populations are variable, especially in degree of tomentose pubescence. This character is probably most important on leaf surfaces, petioles, and calyx.

7. Petal length, particularly in relation to calyx lobes, and petal shape. These are probably not variable in this section.

8. Number of stamens. Probably not variable in this section.

9. Stamen size. Used by Hitchcock (1961) to separate species.

10. Calyx size. This, as an indicator of flower size, might have some value: P. plattensis and P. millefolia usually have larger flowers than some other species of this section.

11. Stipule size and division. Has not been carefully surveyed, however probably does not vary in this section.

From the above discussion of characters, one can see the use of these characters in previous taxonomic classifications. These classifications, however (especially that of Rydberg 1908), have been unsuccessful to one degree or another. In order to provide the basis for a more successful classification in this section, in which apomixis is strongly expected, the relative value of characters must be estimated; no previous workers have quantified (or recorded, except in a very general, qualitative way) the variation of characters within populations or between adjacent ones. Since these ideas are based on the hypothesis of populations with largely apomictic reproduction and an occasional sexual hybridization event, data must be measured showing the occurrence and proximity of other species (even of other sections of the genus) to the population. Two kinds of approach to the problems of analysis of characters will be taken.

I propose to sample populations of species of this section, using about ten characters; two kinds of sample-structure will be used, a square plot (adjusted to include about 25 individual plants of the species in question) for the middle of homogeneous populations, and a transect for long ridges and boundaries between populations. I intend to estimate chromosome number in a rough way by collecting samples of pollen from each plant (see Kohli & Packer 1976); root-tips and/or buds will be fixed from selected individuals as a control on the expected relationship between chromosome number and pollen size.

From these populations sampled, herbarium specimens will be taken of selected individuals as vouchers; a leaf will be collected and dried from selected individuals for analysis by paper chromatography of flavonoids.