Spatial Distributions and Seasonal Changes of Current‐Use Pesticides from the North Pacific to the Arctic Oceans
Bibliographic record
Abstract
Abstract Distributions of eight targeted current‐use pesticides (CUPs) chloroneb, simazine, atrazine, alachlor, dacthal, chlorobenzilate, methoxychlor, and permethrin were investigated in seawater and the atmosphere in a region covering the North Pacific to the Arctic Oceans during the 7th and 8th Chinese National Arctic Research Expedition (2016 and 2017) voyages of the research vessel R/V Xuelong ( Snow Dragon in English). Total CUP concentrations in seawater have prominent seasonal and latitudinal trends, with higher concentrations occurring at lower latitudes in early summer. The major contributors of the CUPs (∑ 8 CUP) did not alter over different seasons with dominant chloroneb, alachlor, and atrazine accounting for more than 90%, but their concentrations did have marked seasonal changes. However, the compositions of the eight analyzed CUPs varied indistinctly between seasons indicating a possible combined environmental impact on these compounds rather than the effects of individual chemical properties. Three‐day backward air mass trajectories indicate that atmospheric masses from northeastern China are responsible for the high concentrations of CUPs in East China and Japan Seas, whereas those from the North Atlantic Ocean contribute to the low levels in local area. Fugacity ratios indicate potential volatilization and equilibrium of chloroneb in the Canada Basin of the Arctic Ocean and Japan Sea, respectively, and deposition of other CUPs in both regions. However, atmospheric concentrations are decoupled from those in seawater, which indicates a low exchange rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".