Helping authors produce FAIR taxonomic data: evaluation of an author-driven phenotype data production prototype
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
It is well-known that the use of vocabulary in phenotype treatments is often inconsistent. An earlier survey of biologists who create or use phenotypic characters revealed that this lack of standardization leads to ambiguities, frustrating both the consumers and producers of phenotypic data. Such ambiguities are challenging for biologists, and more so for Artificial Intelligence, to resolve. That survey also indicated a strong interest in a new authoring workflow supported by ontologies to ensure published phenotype data are FAIR (Findable, Accessible, Interoperable, and Reusable) and suitable for large-scale computational analyses. In this article, we introduce a prototype software system designed for authors to produce computational phenotype data. This platform includes a web-based, ontology-enhanced editor for taxonomic characters (Character Recorder), an Ontology Backend holding standardized vocabulary (the Cared Ontology), and a mobile application for resolving ontological conflicts (Conflict Resolver). We present two formal user evaluations of Character Recorder, the main interface authors would interact with to produce FAIR data. The evaluations were conducted with undergraduate biology students and Carex experts. We evaluated Character Recorder against Microsoft Excel on their effectiveness, efficiency, and the cognitive demands of the users in producing computable taxon-by-character matrices. The evaluations showed that Character Recorder is quickly learnable for both student and professional participants, with its cognitive demand comparable to Excel's. Participants agreed that the quality of the data Character Recorder yielded was superior. Students praised Character Recorder's educational value, while Carex experts were keen to recommend it and help evolve it from a prototype into a comprehensive tool. Feature improvements recommended by expert participants have been implemented after the evaluation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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