Use of molecular genetics for understanding seabird evolution, ecology and conservation
Bibliographic record
Abstract
Information on genetic variation within and among populations of highly mobile organisms such as seabirds is necessary for understanding their evolution and ecology, and can be a tool for conservation.Recent developments in molecular genetics, including efficient mutationdetection methods and automated sequencing, are providing detailed genetic information for non-model organisms.Furthermore, theoretical advances such as coalescent theory and molecular assignments are providing powerful tools to determine species' historical and contemporary abundance, distributions and movements.We review advances for studying phylogenetics, population genetics, hybridization, ecology and conservation in seabirds and summarize recent studies in each field.All fields will benefit from larger data sets and more sophisticated analytical methods.Phylogenetic studies will provide a more robust determination of evolutionary history, while studies of population genetics and hybridization will be elevated to genomic-level avenues of inquiry.Ecological studies may benefit from improved molecular assignments, and conservationfocussed studies will benefit from an increased understanding of seabird evolution and ecology.In addition, we highlight that combination of new molecular and analytical tools with data on morphology, behaviour and movements is especially powerful for understanding seabird evolution and ecology, and for aiding conservation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| 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".