Changes in Dissolved Organic Matter (DOM) Fluorescence in Proglacial Antarctic Streams
Bibliographic record
Abstract
Dissolved organic matter (DOM) exported in glacier meltwater influences downstream biogeochemical processes, and climate warming may increase the meltwater DOM flux. In this study, we quantify and use fluorescence spectroscopy and parallel factor analysis (PARAFAC) to characterize DOM exported in glacier meltwater in Taylor and Wright Valleys, McMurdo Dry Valleys, Antarctica.Andersen Creek, a proglacial stream draining Canada Glacier meltwater, exported ∼20 kg of DOM as dissolved organic carbon (DOC) to Lake Hoare during the melt season. Supraglacial snowpack DOM from all of the Dry Valley glaciers sampled exhibits “protein-like” fluorescence indicating that it contains potentially labile moieties that might be a favorable substrate supporting downstream microbial metabolism. However, this protein-like fluorescence does not persist in any of the meltwater streams surveyed, suggesting that it is quickly transformed by resident microbial populations and/or mixed with other DOM pools within stream channels or along its margins.Given the seemingly ubiquitous protein-like fluorescent characteristic of glacier-derived DOM and the immediate change to the bulk DOM in glacier streams, a more thorough characterization of the bulk DOM pool from glacial sources and through downstream ecosystems would yield valuable information about the potential contribution of glacier melt to the global carbon cycle.
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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 | 0.000 | 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) | 0.000 | 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".