Bibliographic record
Abstract
Recent work has highlighted the importance of the so-called “weathering crust” as a microbially and hydrologically active layer on glacier surfaces. However, this layer is yet to undergo investigation, with no estimates of water, microbial or nutrient fluxes through it to downstream freshwater and marine ecosystems. The mechanics of the weathering crust, and its role in transport and/or retention of particulate impurities at the glacier surface presents a research imperative. To investigate the eco-hydrology of glacier surfaces, this thesis presents a dataset collected at eleven sites in the Northern Hemisphere from the Canadian Arctic to the European Alps, collected between 2014 and 2016. To interrogate this dataset, the study develops and tests a novel logging piezometer which is used to calculate mean weathering crust hydraulic conductivity at all locations of 0.184 m d-1, equivalent to a sandstone, and meltwater velocities of 10-1 m d-1. This hydrologically poor aquifer, causes the storage of water at the surface for tens of day, providing an ideal medium for biogeochemical cycling. For microbial cell enumeration, a flow cytometry protocol is presented which is suitable for glacial environments providing accurate, reliable cell counts. Across the eleven sites, mean microbial cell concentration in weathering crust meltwater was revealed to be ≈ 104 cells mL-1 . It was unclear what controls exist upon cell concentrations in the weathering crust, however no links between weathering crust hydraulic conductivity, electrical conductivity or water temperature and cell concentrations were observed. Cellular particulate organic carbon flux (POC) form this active environment contributes a minimum of 1.1 Tg of cellular carbon per year to downstream freshwater and marine environments per year.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".