Bibliographic record
Abstract
Ecology is the study of the relationships between organisms and their environments, whereas ecological genetics focuses more specifically on the genetics of ecologically important traits, i.e., traits that influence ecological relationships. At its inception, ecological genetics focused particularly on traits that influence fitness, such as those that affect survival and reproduction. This focus has been maintained, although ecological genetics now also investigates the ecological and evolutionary processes that influence patterns of genetic variation in natural populations. Therefore, it can also be considered a study of genetic processes associated with microevolutionary change. Although both Charles Darwin and Alfred Russel Wallace brought together ecological and genetic concepts in the nineteenth century, the term “ecological genetics” was first used by E. B. Ford in his groundbreaking book Ecological Genetics (Ford 1964, cited under General Overviews: Textbooks). The field has evolved considerably since that time and now overlaps substantially with molecular ecology, a closely related field that uses molecular genetic tools to study questions in ecology. The only real difference between molecular ecology and ecological genetics is that the latter is not limited to studies based on molecular genetics. Instead, the term “ecological genetics” can refer to any study of the genetics of natural populations, whether they are based on molecular genetics, population genetics, or quantitative genetics. However, molecular genetic techniques are increasingly accessible and increasingly informative, and they often provide a relatively fast and cost-effective way to get data. As a result, the majority of ecological genetic studies now incorporate a combination of field and molecular genetic data, and the functional line between ecological genetics and molecular ecology is increasingly blurred.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.031 |
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".