Finding the Missing Piece: Development and Applications of a Data-driven Strategy for Imputation of Mixed-type Trait Datasets
Bibliographic record
Abstract
Missing values are a prevalent issue in trait datasets and present methodological challenges for researchers. Data may be more complete for larger, charismatic species and less available for smaller-bodied species or those inhabiting understudied regions, which may result in biased inferences when datasets are reduced to species or groups with complete data. Imputation methods offer an alternative to complete-case analysis as they estimate the missing values using observed data, thereby retaining the sample size and statistical power of the study. Phylogenetic imputation methods build upon this concept by co-opting the phylogenetic signal in trait data to improve imputation performance. However, current guidelines for imputation are limited to select taxa and numerical and/or simulated data, and the performances of imputation methods using real, mixed-type (numerical and categorical) trait data are untested. To address these issues, the first study presents a real data-driven simulation strategy for imputation method selection for a given mixed-type dataset. The strategy entails missingness simulations, performance evaluations of candidate methods with and without phylogeny, and application of the best-suited method to test the impact of imputation on target dataset distributions and characteristics. Results indicate that a data-driven method selection approach reduces imputation error and preserves important dataset properties. The second study applies this strategy to datasets of diverse vertebrate groups, testing the performances of imputation methods using real mixed-type biological and environmental traits for 21 taxonomic orders. The comparatively strong performance of Random Forest imputation is apparent across the dataset types, and phylogenetic information appears generally advantageous. However, the exceptions (where imputation performs poorly) are striking and underscore the importance of a data-driven approach toward method selection. The third study illustrates the impact of imputation on biological inferences using a molecular evolution case study. Correlates of molecular rates in fishes are investigated using a complete-case dataset and datasets imputed with and without phylogeny. The role that method choice plays is further illuminated here, as imputed values have a considerable impact on derived inferences. Overall, this thesis contributes a novel strategy and suggestions for trait imputation that span diverse data types at unprecedented taxonomic levels, facilitating new research directions.
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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 | 0.056 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".