Exploring the potential influence of climate change and particulate organic carbon scenarios on the fate of neutral organic contaminants in the Arctic environment
Bibliographic record
Abstract
The main objective of this study is to explore the potential influence of climate change and particulate organic carbon scenarios on the fate of organic chemicals in the Arctic marine environment using an evaluative modeling approach. Particulate organic carbon scenarios are included to represent changes such as enhanced primary production and terrestrial inputs. Simulations are conducted for a set of hypothetical chemicals covering a wide range of partitioning property combinations using a 40-year emission scenario. Differences in model output between the default simulations (i.e. contemporary conditions) and future scenarios during the primary emission phase are limited in magnitude (typically within a factor of two), consistent with other modeling studies. The changes to particulate organic carbon levels in the Arctic Ocean assumed in the simulations exert a relatively important influence for hydrophobic organic chemicals during the primary emission phase, mitigating the potential for exposure via the pelagic food web by reducing freely-dissolved concentrations in the water column. The changes to particulate organic carbon levels are also influential in the secondary emission/depuration phase. The model results illustrate the potential importance of changes to organic carbon levels in the Arctic Ocean and support efforts to improve the understanding of organic carbon cycling and links to climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".