Arctic microbial ecosystems and impacts of extreme warming during the International Polar Year
Bibliographic record
Abstract
As a contribution to the International Polar Year program MERGE (Microbiological and Ecological Responses to Global Environmental change in polar regions), studies were conducted on the terrestrial and aquatic microbial ecosystems of northern Canada (details at: http://www.cen.ulaval.ca/merge/). The habitats included permafrost soils, saline coldwater springs, supraglacial lakes on ice shelves, epishelf lakes in fjords, deep meromictic lakes, and shallow lakes, ponds and streams. Microbiological samples from each habitat were analysed by HPLC pigment assays, light and fluorescence microscopy, and DNA sequencing. The results show a remarkably diverse microflora of viruses, Archaea (including ammonium oxidisers and methanotrophs), Bacteria (including filamentous sulfur-oxidisers in a saline spring and benthic mats of Cyanobacteria in many waterbodies), and protists (including microbial eukaryotes in snowbanks and ciliates in ice-dammed lakes). In summer 2008, we recorded extreme warming at Ward Hunt Island and vicinity, the northern limit of the Canadian high Arctic, with air temperatures up to 20.5 °C. This was accompanied by pronounced changes in microbial habitats: deepening of the permafrost active layer; loss of perennial lake ice and sea ice; loss of ice-dammed freshwater lakes; and 23% loss of total ice shelf area, including complete break-up and loss of the Markham Ice Shelf cryo-ecosystem. These observations underscore the vulnerability of Arctic microbial ecosystems to ongoing 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".