Modeling Taxon Concepts: A new approach to an old problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although the biodiversity informatics community has recognized and understood the complexity of modeling information about scientific names and associated taxonomic concepts for more than three decades, many of the original questions and problems remain unresolved today. Because most biodiversity data is anchored to scientific names, and these names are governed by Codes of nomenclature, most effort and progress has focused on data structures centered around scientific names, rather than taxonomic concepts. But, as has been well documented in biodiversity data standards communities (e.g., Berendsohn (1995), Patterson et al. (2010), Pyle et al. (2021)), the relationship between the text-string scientific-name labels and the circumscribed conceptual taxa they are intended to represent is highly imprecise. Many attempts have been made to develop data models to represent taxonomic concepts as discrete, identifiable units to which biodiversity data can be linked. However, none has gained wide-spread adoption, often due to inherent subjective interpretations and the degree of taxonomic expertise required to define and interpret the individual units – aspects that limit their practical scalability. Similarly, previous efforts to develop taxon concept data models conflate properties of circumscription, classification, and nomenclature, resulting in overloaded notions of taxa that quickly become intractable. We describe an approach that mirrors centuries of actual taxonomic practice, rooted in fundamental properties of Code-regulated scientific names, which can leverage sources of existing digital information to represent taxonomic concepts in a highly structured, objective and computable way. It isolates the properties of circumscription from those of classification and nomenclature, but enables algorithmic integration of these three separate facets of taxonomic information using consistent informatic structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 it