Modeling historical budget for β-Hexachlorocyclohexane (HCH) in the Arctic Ocean: A contrast to α-HCH
Bibliographic record
Abstract
The historical annual loading to, removal from, and cumulative burden in the Arctic Ocean for β-hexachlorocyclohexane (β-HCH), an isomer comprising 5–12% of technical HCH, is investigated using a mass balance box model from 1945 to 2020. Over the 76 years, loading occurred predominantly through ocean currents and river inflow (83%) and only a small portion via atmospheric transport (16%). β-HCH started to accumulate in the Arctic Ocean in the late 1940s, reached a peak of 810 t in 1986, and decreased to 87 t in 2020, when its concentrations in the Arctic water and air were ∼30 ng m−3 and ∼0.02 pg m−3, respectively. Even though β-HCH and α-HCH (60–70% of technical HCH) are both the isomers of HCHs with almost identical temporal and spatial emission patterns, these two chemicals have shown different major pathways entering the Arctic. Different from α-HCH with the long-range atmospheric transport (LRAT) as its major transport pathway, β-HCH reached the Arctic mainly through long-range oceanic transport (LROT). The much higher tendency of β-HCH to partition into the water, mainly due to its much lower Henry's Law Constant than α-HCH, produced an exceptionally strong pathway divergence with β-HCH favoring slow transport in water and α-HCH favoring rapid transport in air. The concentration and burden of β-HCH in the Arctic Ocean are also predicted for the year 2050 when only 4.4–5.3 t will remain in the Arctic Ocean under the influence of 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".