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 7 · DO #463 · 204_Popenoe_Bx33FF33r

Collection
Wilson Popenoe (1892–1975) family papers
Item/Folder
The Guava - manuscripts, notes - chapter in outline for proposed book with Herbert Wolfe, n.d.
Digital Object
DO #463, page 7
Collection-level dates
1882–1975
Open PDF at page 7 ↗

Page transcription

6.

Planting and Care

Guava trees are often set too closely together for best production.
If the soil is fertile they should be spaced about 20 feet apart, each way.
Preparation of the land planting should be the same as for orange trees.
Grafted guavas are usually set in the field about a year from the time of
grafting, and should receive the usual watering and mulching after being
planted.

Little research has been done on fertilizing guavas. In many tropical
regions they thrive even when neglected, but of course they grow more
rapidly when given proper cultural attention. On stony or sandy soils in
Florida a fertilizer program suitable for oranges has given excellent
results. The trees should begin to bear fruit in the third year.

Flowers are borne in the leaf axils of new shoots, mostly in late
spring. Fertilizing for vigorous vegetative development assures abundant
bloom. In warm rainy climates flowers will be produced at intervals during
the year, but in regions with marked wet and dry seasons blooming will all
be in the rainy season, unless the trees have been irrigated abundantly
during the dry period.

Little or no pruning is usually given, but where fine large guavas
sell at a premium, some pruning is desirable. Vigorous young shoots produce
the largest fruits, hence moderate heading back and thinning of the
top to develop such shoots is desirable. Root suckers and low-hanging
branches should be removed.

Weed control is less important with guavas than with many other fruits
because of the vigorous root system. Weeds or grass under the trees can
be mowed several times during the harvesting season to facilitate collecting