Bibliographic record
Abstract
Migration is widespread among birds, and the strength of the link between the breeding and wintering grounds, migratory connectivity, influences many ecological and evolutionary processes. Despite its importance, migratory connectivity is poorly estimated for most species. Traditionally, visual observations and bird ringing have been used to monitor migration, but these methods require more effort for relatively little return. Genetic markers and stable isotope signatures have increasingly been used to study connectivity. Each approach has its distinct strengths and weaknesses, and as is often the case, a combination may yield the most insight. In this issue of Molecular Ecology, Rundel and colleagues (2013) present a novel Bayesian statistical framework in which genetics and stable isotope data can be combined to improve the assignment of individuals to different winter or breeding regions. The development of such new statistical methods combined with the increasing number and ease of access of isotopic and genetic data sets will greatly enhance our understanding of migratory connectivity. Add to this the developments of miniature devices to track movements of individuals, and the field is destined to make major progression in the decades to come.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".