A review of marine environmental contaminant issues in the North Pacific: The dangers and how to identify them
Bibliographic record
Abstract
Chemical contaminants in the North Pacific Ocean include hydrocarbons, persistent organic pollutants, metals, persistent solids, and domestic pollutants. Here, we review contaminant research conducted over the past decade, finding that the effects of contaminants cannot be considered in isolation from other major factors causing change to North Pacific ecosystems. Climate change, over-fishing, habitat destruction, eutrophication, and the introduction of exotic species interact with one another and alter contaminant pathways. Climate change and over-fishing are perceived as the main threats to the remote northern marginal seas, the central North Pacific, and the west coast of North America, with contaminants engendering local concern, especially in semi-enclosed bodies of water. Climate change receives less attention in Asian waters, probably because widespread habitat destruction and contamination provide, by themselves, an impending ecological disaster. A systematic approach is urgently required to recognize and prioritize the threats to North Pacific coastal ecosystems. This should include box models, case studies, proxy records, and time series. The ocean should be monitored as a system, including physical media (water, sediment) and the full trophic range of the food web, and tissues should be preserved in archives to provide a resource for understanding emerging concerns. Finally, the development of ecological indicators is urgently required to provide a robust warning system based on the health of the marine ecosystems themselves. It is time to conduct a multi-national assessment of the North Pacific Ocean to develop a common, factual awareness of the threats looming over our coastal waters. Key words: contaminants, climate change, ecosystem change, monitoring, North Pacific, trends.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".