Conservation of North American migratory birds: insights from developments in tracking technologies
Bibliographic record
Abstract
Conservation of North American migratory birds requires information about their movements and regulating factors throughout the annual cycle. Over the past 10 or more years, improvements in tracking technology and quantitative approaches to assessing resulting data have yielded advances in understanding many aspects of North American bird migration with relevance to conservation. To date, much of the synthesis of this information has focused on describing patterns and drivers of migration without directly addressing how these advances can inform migratory bird conservation. We begin by describing broad patterns of migration behavior observed in North American birds and briefly summarize the technological advances that have characterized different eras of bird migration research that have provided data relevant to conservation. We then illustrate how data derived from migration studies can inform conservation strategies, including addressing regulating factors outside the breeding period for North American migratory birds, and highlight how different types of migration data have shaped conservation of three well-studied species. Lastly, we discuss critical knowledge gaps and future directions for research needed to better inform North American migratory bird conservation. In particular, we highlight how further technological developments could contribute to the development of effective conservation action in the context of climate change. We also recommend that future research and conservation efforts incorporate means of evaluating the success of conservation actions that target North American migratory birds outside the breeding period.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".