Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many marine ecologists aspire to use genetic data to understand how selection and demographic history shape the evolution of diverging populations as they become reproductively isolated species. I propose combining two types of genetic analysis focused on this key early stage of the speciation process to identify the selective agents directly responsible for population divergence. Isolation-with-migration (IM) models can be used to characterize reproductive isolation between populations (low gene flow), while codon models can be used to characterize selection for population differences at the molecular level (especially positive selection for high rates of amino acid substitution). Accessible transcriptome sequencing methods can generate the large quantities of data needed for both types of analysis. I highlight recent examples (including our work on fertilization genes in sea stars) in which this confluence of interest, models, and data has led to taxonomically broad advances in understanding marine speciation at the molecular level. I also highlight new models that incorporate both demography and selection: simulations based on these theoretical advances suggest that polymorphisms shared among individuals (a key source of information in IM models) may lead to false-positive evidence of selection (in codon models), especially during the early stages of population divergence and speciation that are most in need of study. The false-positive problem may be resolved through a combination of model improvements plus experiments that document the phenotypic and fitness effects of specific polymorphisms for which codon models and IM models indicate selection and reproductive isolation (such as genes that mediate sperm-egg compatibility at fertilization).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it