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 5 · DO #1408 · 207_Kelso_r

Collection
Leon Hugh Kelso (1907–1982) papers
Item/Folder
Papers, 1929–1976, n.d.
Digital Object
DO #1408, page 5
Collection-level dates
1929–1974, n.d.
Open PDF at page 5 ↗

Page transcription

-1-
BIOLOGICAL LEAFLET NO. 106 L. K. Issued November, 1974

Classification of Vegetation.

Principles of classification and classification systems
of various phytocenological schools. Transl. L. Kelso
V. D. Aleksandrova. "Nauka"Publishing House. Leningrad. 1969.

WHAT IS CLASSIFICATION? p. 5-11

There is no problem in geobotany which can evoke so much
discussion and so much contradictory literature as the subject of
classification of vegetation. In the scientific world there is no
unity of opinion, either as to the basic principles of classifica-
tion nor in makeup of a classification system, nor in modes of
gathering field material and its treatment. The disagreement
involves both the basic idea as well as the methods, and the
sharply discordant terminology. To look into this complex situation
critically, one must define first of all what we mean by classifica-
tion, what tasks are undertaken in classification, and what
requirements are satisfactory for these tasks.

Classification is a logical operation which involves the
separation of a given multiple of objects into submultiples,
or classes wherein by class we mean an aggregation of objects
having common characters distinguishing objects of the given
class from other objects which do not have such characters.
Classification of plants for example is defined as a "logical
operation consisting of separation of the whole multiple of
organisms examined according to their constituent resemblances
and differences into separate submultiples or groups, called
taxons." (Taktadzhyan, 1966: 34.) Notwithstanding the consistent
logical basis of procedures of classification for any objects,
classification of plant communities has, in comparison to