Effects of retrogressive permafrost thaw slumping on sediment chemistry and submerged macrophytes in Arctic tundra lakes
Bibliographic record
Abstract
Summary 1. Global warming is predicted to cause changes in permafrost cover and stability in the Arctic. Zones of high ion concentration in regions of ice‐rich permafrost are a reservoir of chemicals that can potentially be transferred to fresh waters during thawing. Consequently, input of enriched runoff from the thaw and sediment and vegetation from the landscape could alter lakes by affecting their geochemistry and biological production. 2. Three undisturbed lakes and five lakes disturbed by retrogressive permafrost thaw slumps were sampled during late summer of 2006 to assess the potential effects of thermokarst shoreline slumping on water and sediment chemistry, the underwater light regime, and benthic macrophyte biomass and community structure. 3. Undisturbed lakes had sediments rich in organic material and selected micronutrients, while disturbed lakes had sediments richer in calcium, magnesium and strontium, greater transparency of the water column, and a well‐developed submerged macrophyte community. 4. It is postulated that enriched runoff chemistry may alter nutrient availability at the sediment–water interface and also the degradation of organic material, thus affecting lake transparency and submerged macrophytes. The results suggest that retrogressive permafrost slumping can significantly affect food webs in arctic tundra lakes through an increase in macrophyte biomass and development of a more complex benthic habitat.
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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".