HYBRIDIZATION LEADS TO SENSORY REPERTOIRE EXPANSION IN A GYNOGENETIC FISH, THE AMAZON MOLLY (<i>POECILIA FORMOSA</i>): A TEST OF THE HYBRID-SENSORY EXPANSION HYPOTHESIS
Bibliographic record
Abstract
Expansions in sensory systems usually require processes such as gene duplication and divergence, and thus evolve slowly. We evaluate a novel mechanism leading to rapid sensory repertoire expansion: hybrid-sensory expansion (HSE). HSE occurs when two species with differently tuned sensory systems form a hybrid, bringing together alleles from each of the parental species. In one generation, a sensory repertoire is created that is the sum of the variance between parental species. The Amazon molly presents a unique opportunity to test the HSE hypothesis in a "frozen" hybrid. We compared opsin sequences of the Amazon molly, Poecilia formosa, to those of the parental species. Both parental species are homozygous at the RH2-1 locus and each of the four long wavelength sensitive loci, while P. formosa possess two different alleles at these loci; one matching each parental allele. Gene expression analysis showed P. formosa use the expanded opsin repertoire that was the result of HSE. Additionally, behavioral tests revealed P. formosa respond to colored stimuli in a manner similar or intermediate to the parental species P. mexicana and P. latipinna. Together these results strongly support the HSE hypothesis. Hybrid-sensory repertoire expansion is likely important in other hybrid species and in other sensory systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".