Snowpack changes around a nickelcopper smelter at Monchegorsk, northwestern Russia
Bibliographic record
Abstract
Snow depth in industrial barrens adjacent to the nickelcopper smelter at Monchegorsk (Kola peninsula, northwestern Russia) by the end of the winter was reduced to one-third of the depth observed in weakened and healthy forests located 3065 km from the smelter; this reduction was due to both decline (by one-half) in the amount (mass) of snow and increase in snow density. Since winter precipitation in Monchegorsk was about the same as in an unpolluted locality 56 km south-southwest of the smelter, and snowpack characteristics correlated with site-specific wind speed, the low amount of snow around the smelter is presumably due to snow movement from open windy habitats and enhanced snow evaporation during the wind transport; higher snow densities may be explained by wind-induced compaction of snow particles. Pollution affects snowpack characteristics by modifying wind regime via forest damage; in turn, decline in snow depth influence the growth form and (possibly) performance of trees that managed to survive in heavily polluted habitats. Thus, initial (partially pollution-induced) forest disturbance, through secondary effects, may enhance further disturbance in a positive feedback fashion; therefore, possible ecological effects of pollution-related snowpack changes should be accounted for in field studies conducted along pollution gradients within the forest zone.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".