The ambiguity of “hybrid swarm”: inconsistent definitions and applications in existing research
Bibliographic record
Abstract
1 Abstract Hybridization is common in wild taxa and often increases in frequency following anthropogenic disturbance to an environment. Next-generation sequencing techniques make genomic analysis of a large number of individuals feasible, vastly improving the analysis for and promoting a greater frequency of studies on hybridization. However, terminology surrounding hybridization can be inconsistent; in particular, the term “hybrid swarm” has been used extensively in the literature but lacks a consistent definition. In this paper, we conducted a comprehensive review of the literature that uses the term “hybrid swarm” in reference to hybridization between taxa and challenged putative definitions of the term. We found that the term “hybrid swarm” is used in a variety of contexts, including some contradictory to other literature, and that there is little consensus on what constitutes a hybrid swarm in terms of hybrid outcomes, frequency relative to disturbances, or duration of existence. We dissuade researchers from use of the term “hybrid swarm” and instead suggest more specific and clear terminology to describe aspects of hybridization. Consequently, we hope that this paper promotes consensus surrounding hybridization terminology and improves the quality of future research on 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 | 0.086 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".