Giant taxon‐character matrices <scp>II</scp>: a response to Laing et al. (2017)
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
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it