Host–parasite interactions and the evolution of ploidy
Bibliographic record
Abstract
Although the majority of animals and plants, including humans, are dominated by the diploid phase of their life cycle, extensive diversity in ploidy level exists among eukaryotes, with some groups being primarily haploid whereas others alternate between haploid and diploid phases. Previous theory has illuminated conditions that favor the evolution of increased or decreased ploidy but has shed little light on which species should be primarily haploid and which primarily diploid. Here, we report a discovery that emerged from host-parasite models in which ploidy levels were allowed to evolve: selection is more likely to favor diploidy in host species and haploidy in parasite species. Essentially, when parasites must evade a host's immune system or defense response, selection favors parasitic individuals that express a narrow array of antigens and elicitors, thus favoring haploid parasites over diploid parasites. Conversely, when hosts must recognize a parasite before mounting a defensive response, selection favors hosts with a broader arsenal of recognition molecules, thus favoring diploid hosts over haploid hosts. These results are consistent with the predominance of haploidy among parasitic protists.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".