Grand Challenges in Migration Biology
Bibliographic record
Abstract
Billions of animals migrate each year. To successfully reach their destination, migrants must have evolved an appropriate genetic program and suitable developmental, morphological, physiological, biomechanical, behavioral, and life-history traits. Moreover, they must interact successfully with biotic and abiotic factors in their environment. Migration therefore provides an excellent model system in which to address several of the "grand challenges" in organismal biology. Previous research on migration, however, has often focused on a single aspect of the phenomenon, largely due to methodological, geographical, or financial constraints. Integrative migration biology asks 'big questions' such as how, when, where, and why animals migrate, which can be answered by examining the process from multiple ecological and evolutionary perspectives, incorporating multifaceted knowledge from various other scientific disciplines, and using new technologies and modeling approaches, all within the context of an annual cycle. Adopting an integrative research strategy will provide a better understanding of the interactions between biological levels of organization, of what role migrants play in disease transmission, and of how to conserve migrants and the habitats upon which they depend.
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".