The relationship between plastic ingestion and trace element concentrations in Arctic seabirds
Bibliographic record
Abstract
Seabirds ingest contaminants linked to their prey's tissues, but also adsorbed to ingested plastic debris. To explore relationships between ingested plastics and trace elements concentrations, we analyzed 25 essential non-essential trace elements in liver tissue in relation to plastic content in the gastrointestinal tract in adults of four species of Arctic seabirds with different propensity to ingest plastic. Linear Discriminant Analysis (LDA) provided a clear separation between species based on element concentrations, but not among individuals with and without plastics. Molybdenum, copper, vanadium, and zinc were strong drivers of the LDA, separating northern fulmars (Fulmarus glacialis) from other species (60.4 % of explained between-group variance). Selenium, vanadium, zinc, and mercury were drivers separating black-legged kittiwakes (Rissa tridactyla) from the other species (19.3 % of explained between-group variance). This study suggests that ingestion of plastic particles has little influence on the burden of essential and non-essential trace elements in Arctic seabird species.
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 | 0.000 | 0.000 |
| 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".