Hydrological controls on glacially exported microbial assemblages
Bibliographic record
Abstract
Abstract The Greenland Ice Sheet (GrIS) exports approximately 400 km3 of freshwater annually to downstream freshwater and marine ecosystems. These meltwaters originate in a wide range of well‐defined habitats that can be associated with very different physical environments within the ice sheet, ranging from oxygenated surface environments that are exposed to light and supplied with nutrients from atmospheric/aeolian sources to subglacial environments that are permanently dark, isolated from the atmosphere, and potentially anoxic. Hydrological conditions in the latter likely favor prolonged rock‐water contact. The seasonally evolving hydrological system that drains meltwaters from the GrIS connects these distinct microbial habitats and exports the microbes contained within them to downstream ecosystems. The microbial assemblages exported in glacier meltwater may have an impact on downstream ecosystem function and development. We explored how the seasonal development of a glacial drainage system influences the character of microbial assemblages exported from the GrIS by monitoring the seasonal changes in hydrology, water chemistry, and microbial assemblage composition of meltwaters draining from a glacier in southwest Greenland. We found that the microbial assemblages exported in meltwaters varied in response to glacier hydrological flow path characteristics. Whether or not meltwaters passed through the subglacial environment was the first‐order control on the composition of the microbial assemblages exported from the glacier, while water source (i.e., supraglacial or extraglacial) and subglacial residence times were second‐order controls. Glacier hydrology therefore plays a fundamental role in determining the microbial exports from glaciated watersheds.
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 | 0.000 |
| Science and technology studies | 0.000 | 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".