Morphological variability or inter‐observer bias? A methodological toolkit to improve data quality of multi‐researcher datasets for the analysis of morphological variation
Bibliographic record
Abstract
OBJECTIVES: The investigation of morphological variation in animals is widely used in taxonomy, ecology, and evolution. Using large datasets for meta-analyses has dramatically increased, raising concerns about dataset compatibilities and biases introduced by contributions of multiple researchers. MATERIALS AND METHODS: We compiled morphological data on 13 variables for 3073 individual mouse lemurs (Cheirogaleidae, Microcebus spp.) from 25 taxa and 153 different sampling locations, measured by 48 different researchers. We introduced and applied a filtering pipeline and quantified improvements in data quality (Shapiro-Francia statistic, skewness, and excess kurtosis). The filtered dataset was then used to test for genus-wide sexual size dimorphism and the applicability of Rensch's, Allen's, and Bergmann's rules. RESULTS: Our pipeline reduced inter-observer bias (i.e., increased normality of data distributions). Inter-observer reliability of measurements was notably variable, highlighting the need to reduce data collection biases. Although subtle, we found a consistent pattern of sexual size dimorphism across Microcebus, with females being the larger (but not heavier) sex. Sexual size dimorphism was isometric, providing no support for Rensch's rule. Variations in tail length but not in ear size were consistent with the predictions of Allen's rule. Body mass and length followed a pattern contrary to predictions of Bergmann's rule. DISCUSSION: We highlighted the usefulness of large multi-researcher datasets for testing ecological hypotheses after correcting for inter-observer biases. Using genus-wide tests, we outlined generalizable patterns of morphological variability across all mouse lemurs. This new methodological toolkit aims to facilitate future large-scale morphological comparisons for a wide range of taxa and applications.
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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 | 0.367 | 0.600 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".