Characterization of dissolved organic matter (DOM) from glacial environments using total fluorescence spectroscopy and parallel factor analysis
Bibliographic record
Abstract
Abstract Aquatic dissolved organic matter (DOM) is a major reservoir of reduced organic carbon and has a significant influence on heterotrophic biological productivity and water quality in marine and freshwater environments. Although the forms and transformations of DOM in temperate aquatic and soil environments have been studied extensively, this is not the case for glacial environments. In this study, fluorescent excitation–emission matrices (EEMs), parallel factor analysis (PARAFAC) and cluster analysis were used to characterize the fluorescing components of DOM in ice and water samples from supraglacial, englacial, subglacial and proglacial environments of seven glaciers in the Canadian Arctic, Norway and Antarctica. At least five significant fluorescent DOM fractions were identified, which accounted for 98.2% of the variance in the dataset. These included four protein-like components and one humic-like component. The predominantly proteinaceous character of DOM from these glaciers is very different from the more humic character of DOM described previously from lacustrine, fluvial, estuarine and marine environments. DOM from the sampled glaciers is broadly similar in character despite their geographically distinct locations, different thermal regimes and inter- and intra-site differences in potential organic matter sources. Glacier ice samples had a relatively low ratio of humic-like :protein-like fluorescence while meltwater samples had a higher ratio.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".