Defining what constitutes a reliable dataset to test for hybridization and introgression in marine zooplankton: Comment on Choquet et al. 2020 “No evidence for hybridization between <scp><i>Calanus finmarchicus</i></scp> and <scp><i>C. glacialis</i></scp> in a subarctic area of sympatry”
Bibliographic record
Abstract
Abstract The article ‘No evidence for hybridization between Calanus finmarchicus and C. glacialis in a subarctic area of sympatry’ (Choquet et al. 2020) concludes that “no evidence supports a potential for hybridization between C. finmarchicus and C. glacialis”. We argue that the InDel markers used by Choquet et al. (2020) may have limited capacity to detect admixed genotypes between C. finmarchicus and C. glacialis due to an inappropriate choice of reference sample for each species during the marker development. We first review terms and concepts used in genetic classification using reference samples and describe problems associated with the selection of genetic markers in the context of possible hybridization. We reanalyze InDel genotypes provided with Choquet et al. (2017) and identified an admixed individual. We then contrast methods used by Choquet et al. (2017) and Parent et al. (2012) and explain how Parent et al. (2012) developed microsatellite markers capable of discriminating admixed genotypes from parental species. In this comment, we have identified a major issue that must be considered when selecting reference samples in the context of testing for possible hybridization.
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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 | 0.152 | 0.400 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".