Authors’ attitude toward adopting a new workflow to improve the computability of phenotype publications
Bibliographic record
Abstract
Critical to answering large-scale questions in biology is the integration of knowledge from different disciplines into a coherent, computable whole. Controlled vocabularies such as ontologies represent a clear path toward this goal. Using survey questionnaires, we examined the attitudes of biologists toward adopting controlled vocabularies in phenotype publications. Our questions cover current experience and overall attitude with controlled vocabularies, the awareness of the issues around ambiguity and inconsistency in phenotype descriptions and post-publication professional data curation, the preferred solutions and the effort and desired rewards for adopting a new authoring workflow. Results suggest that although the existence of controlled vocabularies is widespread, their use is not common. A majority of respondents (74%) are frustrated with ambiguity in phenotypic descriptions, and there is a strong agreement (mean agreement score 4.21 out of 5) that author curation would better reflect the original meaning of phenotype data. Moreover, the vast majority (85%) of researchers would try a new authoring workflow if resultant data were more consistent and less ambiguous. Even more respondents (93%) suggested that they would try and possibly adopt a new authoring workflow if it required 5% additional effort as compared to normal, but higher rates resulted in a steep decline in likely adoption rates. Among the four different types of rewards, two types of citations were the most desired incentives for authors to produce computable data. Overall, our results suggest the adoption of a new authoring workflow would be accelerated by a user-friendly and efficient software-authoring tool, an increased awareness of the challenges text ambiguity creates for external curators and an elevated appreciation of the benefits of controlled vocabularies.
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.
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 | 0.087 | 0.241 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".