Building the “Plant Glossary”—A controlled botanical vocabulary using terms extracted from the Floras of North America and China
Bibliographic record
Abstract
Abstract Taxonomic descriptions contain valuable phenotypic data that is often not directly accessible for modern evolutionary, ecological, or biodiversity analyses. We describe a process for building a consensus‐based controlled vocabulary from taxonomic descriptions for plants, which also can be applied for building controlled vocabularies for other taxon groups. Controlled vocabularies are useful as lexicons for text mining algorithms, as source of candidate terms for ontologies, and as guides to help future authors use domain vocabulary more appropriately and consistently. We extracted phenotype‐ describing phrases terms from descriptions of 30 volumes of the Flora of North America and Flora of China and merged these with terms from the Categorical Glossary of the Flora of North America. Seven contributors placed the terms into a set of categories until there was an agreement among two or more categorizations per term. Term categorization makes the meaning of a term more explicit for the subsequent users of the glossary. The resulting “Plant Glossary” (terms and categorization of terms) contains 9228 terms grouped in 53 categories. Differences in term categorization represented 49% of the categorization effort, and the many differences among individual classifications can be attributed to individual interpretation of terms and to the fluid nature of descriptive language used in Floras. The difficulties experienced while classifying the terms allowed us to explore cases where the use of language can hinder the accurate and detailed annotation of taxonomic descriptions. The Plant Glossary represents a significant step towards creating and enriching formal ontologies for plant phenotypes as the semantic phenomena found through this exercise is useful background information for building ontologies. The glossary has been used by new software to parse and annotate plant taxonomic descriptions, and over 6000 new terms are available for creating ontologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".