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 58 · DO #487 · 204_Popenoe_Bx41Volume2r

Collection
Wilson Popenoe (1892–1975) family papers
Item/Folder
Field notes: Mexico, - notebook, letter to Peter Bisset, memorandum, and reprint, 3 February 1919–24 April 1919
Digital Object
DO #487, page 58
Collection-level dates
1882–1975
Open PDF at page 58 ↗

Page transcription

Ideal Mango Special
Moisture 8 to 10%
Ammonia 5 to 6%
Total Phosphoric Acid 7 to 9%
Potash, Retard K2O 9 to 11%
Made from ground bone, waste of soda,
dried blood, dissolved bone black and
high grade potash salts.

Mr. Johnson has 7 trees of an avocado
sent him by C.P. Taft under the name
of Golden. Taft said it was a December
fruit in California. It looks much like
a West Indian. Johnson is now calling it
Maria Theresa. Write Mr. Taft and ask
him if this is not one of the Cuban
seedlings which has fruited at his place.

The method of avocado propagation used on avocados
is a modified saddle
saddle graft. Having the top
the top on the stock plant
wrist after the cone has
united with the stock.
The cion is tied in place with a
narrow strip of rubber running
obtained from Texas Rubber Co.
This tape does not seem to be re-
moved; it rots and breaks off when
the graft commences to swell.
Mr. Howe obtained a high per-
centage of successful grafts by this
method. He grafted his young plants
in the greenhouse in June and
grafted them as soon as they were
about six inches high. An excellent
result is formed.