Reply to the comment by Morales et al. on “Population genetics reveal <i>Myotis keenii</i> (Keen’s myotis) and <i>Myotis evotis</i> (long-eared myotis) to be a single species”
Bibliographic record
Abstract
A.E. Morales et al. (2021. Can. J. Zool. 99(5): 415–422) provided no new evidence to alter the conclusions of C.L. Lausen et al. (2019. Can. J. Zool. 97(3): 267–279). We present background information, relevant comparisons, and clarification of analyses to further strengthen our conclusions. The genesis of the original “evotis–keenii” study in British Columbia (Canada) was to differentiate Myotis keenii (Merriam, 1895) (Keen’s myotis), with one of the smallest North American bat distributions, from sympatric Myotis evotis (H. Allen, 1864) (long-eared myotis), using something other than the suggested post-mortem skull size comparison, but no differentiating trait could be found, leading to the molecular genetics examination of C.L. Lausen et al. (2019). We present cumulative data that rejects the 1979 hypothesis of M. keenii as a distinct species. A.E. Morales et al. (2021) inaccurately portray C.L. Lausen et al.’s (2019) question and results; present inaccurate morphological and outdated distribution data; overstate the impact of homoplasy without supporting evidence; and misinterpret evidence of population structure.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.033 | 0.054 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".