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 14 · DO #4757 · 231_Rogers_Bx5FF7_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Taximetrics Lecture, Berkeley, 1971
Digital Object
DO #4757, page 14
Collection-level dates
1948–1977
Open PDF at page 14 ↗

Page transcription

Page 3

8. Rogers, D. J. and H. S. Fleming. 1972 (In press) Economic Botany. A monograph of Manihot esculenta. The GRAPH model was our chief method to sort out the complex relationships within a cultivated species. The methods of use, and several suggestions on the intraspecific problems of cultivated plant taxonomy are described in sufficient detail so that workers can fully grasp our concepts. Proc. Int. Symp. Trop. Root Crops (Hawaii). pp. 66-70. This interesting application of the character analysis program indicates that it provides another method of classification, in addition to the power as a character analytic technique.

GRAPE

1. Wirth, M., C. F. Estabrook and D. J. Rogers. 1965. A graph theory model for systematic biology, with an example for the Oncidiinae (Orchidaceae). Systematic Zoology 15: 39-69. This paper describes in taxonomic terms the clustering methods developed in the Taximetrics Lab. The paper does not attempt to deal with the mathematical arguments.

2. Estabrook, G. F. and D. J. Rogers. 1966. A general method of taxonomic description for a computed similarity measure. Bioscience 16: 789-793. This paper describes the important process for structuring characters to be used in classification.

3. Estabrook, G. 1966. A mathematical model in graph theory for biological classification. Jour. Theor. Bio. 12: 297-310.

4. Irwin, H. S. and D. J. Rogers. 1967. Monographic studies in Cassia (Leguminosae-Caesalpinioidae). II. A taximetric study of section Apoucouita. Mem. N.Y. Bot. Gard. 16: 71-118. This is one of our first applications of the GRAPH clustering model. The section Apoucouita of Cassia contained some of the most complex classificatory problems in this large genus, and the computer model proved of great assistance in sorting out the specific and sub-specific taxa.

5. France, G. T., D. J. Rogers and I. White. A taximetric study of an angiosperms family: generic delimitation in the Chrysobalanaceae. New Phytol. 68: 1203-1234. Four computer methods were compared for their capacity to make classifications, and our GRAPH model came out on top. In this case, we were comparing genera within a family.

6. Arp, G. and D. J. Rogers. 1970. A computer-aided classification of the varieties of Pediocactus simpsonii (Engelm.) B. & R. Cactus & Succ. Jour. 42: 40-43. An intraspecific study of a cactus species.

7. Stearn, W. T. 1971. A survey of the tropical genera Oplonia and Pallanthele (Acanthaceae). Bull. Br. Mus. (Nat. Hist.) Botany. IV (7): 259-323, plates 38-47. Stearn used our GRAPH model to aid in this study, particularly with the genus Oplonia.