The influence of global climate change on accumulation and toxicity of persistent organic pollutants and chemicals of emerging concern in Arctic food webs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review summarizes current understanding of how climate change-driven physical and ecological processes influence the levels of persistent organic pollutants (POPs) and contaminants of emerging Arctic concern (CEACs) in Arctic biota and food webs. The review also highlights how climate change may interact with other stressors to impact contaminant toxicity, and the utility of modeling and newer research tools in closing knowledge gaps on climate change-contaminant interactions. Permafrost thaw is influencing the concentrations of POPs in freshwater ecosystems. Physical climate parameters, including climate oscillation indices, precipitation, water salinity, sea ice age, and sea ice quality show statistical associations with POPs concentrations in multiple Arctic biota. Northward range-shifting species can act as biovectors for POPs and CEACs into Arctic marine food webs. Shifts in trophic position can alter POPs concentrations in populations of Arctic species. Reductions in body condition are associated with increases in levels of POPs in some biota. Although collectively understudied, multiple stressors, including contaminants and climate change, may act to cumulatively impact some populations of Arctic biota. Models are useful for predicting the net result of various contrasting climate-driven processes on POP and CEAC exposures; however, for some parameters, especially food web changes, insufficient data exists with which to populate such models. In addition to the impact of global regulations on POP levels in Arctic biota, this review demonstrates that there are various direct and indirect mechanisms by which climate change can influence contaminant exposure, accumulation, and effects; therefore, it is important to attribute POP variations to the actual contributing factors to inform future regulations and policies. To do so, a broad range of habitats, species, and processes must be considered for a thorough understanding and interpretation of the consequences to the distribution, accumulation, and effects of environmental contaminants. Given the complex interactions between climate change, contaminants, and ecosystems, it is important to plan for long-term, integrated pan-Arctic monitoring of key biota and ecosystems, and to collect ancillary data, including information on climate-related parameters, local meteorology, ecology, and physiology, and when possible, behavior, when carrying out research on POPs and CEACs in biota and food webs of the Arctic.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it