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 43 · DO #4713 · 231_Rogers_Bx4FF4_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
N. Y. Botanical Garden - Math & Biology, 1964
Digital Object
DO #4713, page 43
Collection-level dates
1948–1977
Open PDF at page 43 ↗

Page transcription

-11-

Goldstine: One has to accept their classifications and see why it works.

Wooster: There is no real grammar for any particular language. Similarly taxonomy is defined as that which the taxonomist can remember.

Macy: One of the unanswered questions is the criteria for knowing when you have as good or better answer.

Cronquist: The present scheme could not be completely at cross purposes with what ought to be the best. Any two things that can cross and produce offspring must be pretty closely related. There is no known example of a hybridization between things which are regularly put into different plant families. As soon as you get to the family level, there are no hybrids. There are some at the genus level. If the scheme were set up wrongly, we would have hybrids between families, between classes, etc.

Rogers: Several of the participants have felt that there is a lack of basic information on the nature of taxonomy. We have therefore drafted Dr. Keck to make a generalized statement about the activities in taxonomy over the years and its present thinking. We appreciate the fact that Dr. Keck is willing to enter the discussion without giving him any advanced warning.

Dr. Keck: Attempts at classification of plants have been made for at least 2000 years, and the first efforts were by the Greeks. The needed classifications were quite simple, and served whatever purposes were required for a number of centuries. More or less intensive investigations of the classifications were begun about 200 years ago, and Linnaeus' works were the most significant attempt to make a more orderly system. Though Linnaeus' taxonomy was, by modern standards, completely artificial, his consistent application of a binomial naming system has proved to be one of the most useful techniques. His work stimulated study by many other biologists, and classification of plants was put onto a reasonable, "natural" basis by the middle of the 19th century, before the publication of Darwin's "Origin of Species." It is interesting to note that, though there was no explanation of why the organisms could be placed together in a natural system, they were more or less organized into essentially an evolutionary system before Darwin's publication. This is not intended to indicate that the system established over a century ago was a perfect reflection of our ideas of evolution of plants today, but only that the evolutionary relationship of many of the major groups of plants were already obvious.

In the analysis of a group of plants for purposes of classification, the taxonomist employs information from the plants in whatever form is useful. In many cases, the establishment of a classification is dependent on information subsidiary to the plants themselves. Thus, geographic distribution and habitat information frequently are fundamental, and the knowledge that one group of organisms may have a world-wide distribution, as is the case of the members of the family Rosaceae, may be critical for understanding the taxonomy of a group.