Time to strengthen and enforce the north american migratory bird treaty act
Bibliographic record
Abstract
Time to strengthen and enforce the north american migratory bird treaty act The number of migratory bird populations is declining; Keith Hobson, professor and research scientist at Environment and Climate Change Canada and Western University, outlines the importance of updating conventions to protect these vulnerable species. One of the great current challenges of effective wildlife conservation globally involves the protection of migratory organisms that cross international boundaries during their annual cycle. The challenge is obvious when considering the myriad of obstacles involved with international agreements and requirements for on-the-ground compliance, especially when considering different languages, cultures and political systems. Nonetheless, such international cooperation is needed now more than ever as migratory animals have been found to be declining at greater rates than their non-migratory counterparts. That is especially the case for migratory birds. Not surprisingly, apart from regulations involving trade in wildlife or wildlife parts, such as the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), few success stories exist.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
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".