How consistent are trait data between sources? A quantitative assessment
Bibliographic record
Abstract
The use of species’ traits is increasing in ecological research. Many studies obtain trait data from a single source, implicitly assuming the accuracy of these data. I critically evaluate this assumption by measuring agreement among sources for trait data. I evaluate inter‐source agreement for 22 traits (anatomical, behavioural, life‐history and niche‐related) among five authoritative data sources (two field guides, two atlases and one online resource) for 263 Canadian butterfly species. This represents the first quantitative comparison of trait data among field guides or atlases. Traits varied considerably in their agreement among sources. Some traits such as wingspan and overwinter stage were fairly consistent among sources, whereas other traits such as habitat breadth were remarkably inconsistent among sources. These findings call into question the reliability of research that relies on a single source for trait data. I offer several recommendations for how trait researchers can account for inter‐source variation in trait data.
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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 | 0.279 | 0.590 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".