The health and habitat use of Glaucous-winged gulls wintering in the Salish Sea
Bibliographic record
Abstract
The Salish Sea is a globally significant location for marine birds. However, forage fish declines, legacy contaminants, and increasing industrial activity are ongoing concerns for wildlife and humans here. Glaucous-winged gulls (Larus glaucescens) are effective biomonitors of long-term shifts in marine food-webs and contaminant trends. Over the past 150 years, they have increasingly relied on terrestrial prey and urban areas to forage and nest. Simultaneously, Glaucous-winged gulls (GWGU) have experienced lower reproductive success and significant population declines. Currently, Environment and Climate Change Canada (ECCC) has a mandate through the Ocean’s Protection Plan to assess threats to wildlife posed by new and proposed industrial projects, including the Trans-Mountain Pipeline expansion which will increase crude oil tanker traffic in the Salish Sea by approximately seven-fold. Canada has a regional stewardship responsibility to GWGUs, and ECCC has identified a gap in knowledge of wintering gull habitat use, diet, contaminant exposure, and health. To address these gaps, we deployed satellite tags on 31 birds and collected blood samples from 164 wintering gulls in 2020 and 2021. Initial findings indicate that 14.8% of GWGU migrated out of the Salish Sea preceding the breeding season (n = 4 of 27). Broadly, gulls appear to have established home ranges, but will make short-distance, temporary migrations to access seasonal resources. Movement patterns among individuals are highly variable, but some appear to specialize on particular anthropogenic habitat types. Additionally, gull health varies regionally within the Salish Sea. Diet and contaminant analyses are on-going but will be used to build a food-web to contaminants model linked with health. This work will help identify important wintering habitat for GWGUs and highlight potential risks posed by anthropogenic and industrial activities. Finally, this research will provide information key to managing the health of coastal seabird populations and the Salish Sea Ecosystem at large.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".