Knot Your Average Bird: A Case Study of the Rufa Red Knot in the Face of Climate Change
Bibliographic record
Abstract
Analyzing the staggering distances traveled by migratory shorebirds, and the challenges faced by these birds during their migration periods, this Article conflates and contrasts the myriad environmental impacts climate change is forcing the globe to contend with. The rufa red knot navigates a migratory path that annually takes it from Tierra del Fuego all the way to Arctic Canada. Because the red knot’s course of migration is so lengthy, and because the number of ecosystems it encounters along the way are so diverse, the red knot is emblematic of the challenges faced by both migratory shorebirds, and the coastal ecosystems they rely upon that are now ever eroding due to climate change. Part I of this Article introduces the rufa red knot. Part II discusses the migration of the red knot and examines many of the challenges faced by the species. Part III analyzes the many threats facing the red knot, including climate change and pollution due to coastal fossil fuel extraction. Part IV introduces an environmental management theory based on “ecoscapes.” Part V discusses the many laws and regulations addressing the threats facing the red knot. Finally, in Part VI the author discusses how proffered solutions can be maximized for all migratory shorebirds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".