Herring gulls, a warning system for global pollutants
Bibliographic record
Abstract
When it comes to monitoring some of the most problematic synthetic chemicals dispersed in the environment, scientists have given the expression “for the birds” a whole new—and more significant—meaning. Herring gulls (Larus argentatus), for example, have a long history of serving as a sentinel species, providing data on pollutants in the Great Lakes region of the U.S. and Canada since the 1970s. Although researchers do collect birds as samples, their primary sampling target is bird eggs. Scientists also turn to Arctic-breeding seabirds such as thick-billed murres (Uria lomvia), northern fulmars (Fulmarus glacialis), black-legged kittiwakes (Rissa tridactyla), glaucous gulls (Larus hyperboreus), and black guillemots (Cepphus grylle). And they’ve carried out long-term investigations of sea eagles in the Baltic Sea. The choice of species to sample often depends on accessibility to remote colonies and the ability to compare data among species around the globe. In addition to tracking contaminant trends, other work
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".