POPs distribution in coastal permafrost soils at Komakuk Beach (Yukon, CA)
Bibliographic record
Abstract
The extensive work in AMAP on Arctic contamination by Persistent Organic Pollutants (POPs) illustrates the strong state of knowledge and data on contaminants in biota and water, but also highlights a stark data gap regarding contaminants in Arctic soils, particularly below the permafrost table. Here, we describe the work carried out on analyses and scaling of contaminants. A key aspect for being able to deliver geospatial databases is identifying which landscape properties we can use to scale the measured contaminant levels. Soils in the Canadian study area at Komakuk Beach (Yukon, CA) have been sampled and analysed for hexachlorobenzene (HCB), 54 polychlorinated biphenyl congeners (PCBs) and 22 individual polycyclic aromatic hydrocarbons (PAHs) in 89 active layer and permafrost core samples. The concentrations have been determined by using Thermo Scientific Dionex ASE 350 and Trace 1310 GC coupled with TSQ9000 TQMS, Thermo Scientific, at CNR-ISP Venice, Italy. The statistical parameters of data on HCB concentrations describe its scarce presence, with limited occurrences linked to the organic rich soil horizons. Overall, Σ54PCBs in soils collected in the catchment area were in the range from 0 to 5.33 pg/g. Both the content of individual PAHs and their total concentration vary widely from 0 to 121ng/g. The contribution of individual PAHs to the total amount is not the same and depends on the concentration of heavy 5–6-nuclear polyarenes and mainly 6-nuclear benzo[ghi]perylene. Our results show that the vertical distribution patterns of light 2–4-nuclear and heavy 5–6-nuclear PAHs are differentiated. Every class of OC is present in the deeper permafrost core portions only in High Centre Polygon (HCP) landform class, highlighting the role of hydrology and percolation in the contaminant distribution in permafrost soils and how these variables are enhanced in (semi-)degraded permafrost landforms. The model equations have been applied on the carbon content prediction maps for each study area, to export a first attempt of upscaling feasible for small coastal catchments in polygonal tundra environment. Contaminant concentrations ranges were calculated for different depth from 0 to 1 meter below the ground, following the carbon content prediction maps from Wagner et al. (sub.)
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 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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".