Giant taxon‐character matrices <scp>II</scp>: a response to Laing et al. (2017)
Bibliographic record
Abstract
The trend towards big data analyses in evolutionary biology has been observed in phylogenetics via the assembly of giant datasets composed of genomic and phenotypic data. We recently (Simões et al., 2017. Giant taxon-character matrices: Quality of character constructions remains critical regardless of size. Cladistics 33, 198-219) presented a critique of the phylogenetic character concepts used in current morphological datasets, with the caution that giant datasets did not obviate the empirical requirement of rigor in character construction. Laing et al. (2017. Giant taxon-character matrices: The future of morphological systematics. Cladistics, https://doi.org/10.1111/cla.12197) have since argued that we had 'suggested' that large datasets inherently contain flawed characters, and that we had presented a substandard methodology of phylogenetic analysis. Laing et al. concluded by discussing their approach to phylogenetic signal, total evidence and the inevitability of large datasets. We here reply to Laing et al. by reviewing what we actually wrote regarding dataset size, characters and methodology. We show that Laing et al.'s. central premise is unsupported, thus characterizing a Straw Man argument, and deeply misrepresents our original study. In part two, we discuss total evidence and phylogenetic signal issues raised by Laing et al. that are of major consequence to the appropriate construction of large morphological datasets.
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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 | 0.060 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.034 | 0.072 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".