Total Evidence, Average Consensus and Matrix Representation with Parsimony: What a Difference Distances Make
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
Matrix representation with parsimony (MRP) can be used to combine trees in the supertree or the consensus settings. However, despite its popularity, it is still unclear whether MRP is really a consensus method or whether it behaves more like the total evidence approach. Previous simulations have shown that it approximates total evidence trees, whereas other studies have depicted similarities with average consensus trees. In this paper, we assess the hypothesis that MRP is equally related to both approaches. We conducted a simulation study to evaluate the accuracy of total evidence with that or various consensus methods, including MRP. Our results show that the total evidence trees are not significantly more accurate than average consensus trees that accounts for branch lengths, but that both perform better than MRP trees in the consensus setting. The accuracy rate of all methods was similarly affected by the number of taxa, the number of partitions, and the heterogeneity of the data.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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