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 9 · DO #976 · 319_Love_Bx1FF21r

Collection
Áskell Löve (1916–1994) papers
Item/Folder
"Wheatgrasses" (3 of 3)
Digital Object
DO #976, page 9
Collection-level dates
1950–1987
Open PDF at page 9 ↗

Page transcription

II.

In the past, the wheats have been recognized as wheat, rye and barley by the ancients, but although they may have been aware of some of the non-cultivated species named them, later generations have not been able to identify such names. Linnaeus named 28 species which he placed in the genus Triticeae, Elymus, Secale, Hordeum and Aegilops, and then might have one species in the genus Bromus; in modern origin these two represent only 22 biological species. Since his time many taxa have been described from within this group, so that presently we recognize almost 500 taxa of which about 320 seem to be at least recently good biological species. It may be of interest at this stage to mention that of these taxa 338, or almost 70%, are known as to their chromosome number, that the karyotype ideogram has been determined for 226, or 45% of them, and that about 100 of these taxa have taken part in about 300 hybrid combinations that have been analyzed as to their meiosis.

No other tribe or group of comparable size has been so intensely studied.

Here I would like to make a brief methodological deviation before continuing a discussion of the classification of these genera, which since Linnaeus have been periodically lumped into a few or split into numerous genera on various considerations.

The method of approach to a scientific problem of this kind is clearly of extreme importance, as is the philosophical thinking behind the conclusions to be drawn, because they will to a large extent determine the type of discovery made or the clarification foregone. Putting the matter the other way round, the method of approach is itself largely dictated by the type of answer you want to obtain: it is, in fact, a kind of question! Furthermore, the question will alter with time and the progress of discovery: when one method has yielded the main crop of answers that it could be expected to provide, it is time to ask another kind of question, by adopting a new method.

The original approach to plant taxonomy inevitably was descriptive because botanists set out to describe as fully and accurately as possible the variation of plants and the phenomena which they exhibit. This approach