Dynamic changes in size‐fractionated dissolved organic matter composition in a seasonally ice‐covered Arctic River
Bibliographic record
Abstract
Abstract Arctic rivers are sensitive to climate and environmental change, but the biogeochemical response remains poorly understood. Monthly size‐fractionated dissolved organic matter (DOM) samples from the lower Yukon River were characterized using UV–visible, fluorescence, and Fourier transform‐infrared (FT‐IR) spectroscopy techniques. The EEM‐PARAFAC analysis revealed three major fluorescent DOM components, including two humic‐like components (C 480 and C 400 ) and one protein‐like component (C 310 ), with their relative importance following the order of C 480 ≥ C 400 > C 310 in the high‐molecular‐weight DOM (1 kDa–0.4 μ m) and C 400 > C 480 > C 310 in the low‐molecular‐weight DOM pool (< 1 kDa). Transformation in DOM and change in sources were manifested in major fluorescent components and optical properties, including biological index (BIX), humification index (HIX), spectral slope ( S 275–295 ) and specific UV absorbance at 254 nm (SUVA 254 ). These changes occurred within different DOM size‐fractions and among ice‐covered, spring freshet, and open seasons. Joint analysis of EEM and FT‐IR spectra using a data fusion technique showed that humic‐like DOM is mostly associated with C─H, C═C, and C─O bonds, while protein‐like DOM is correlated more with C─N and N─H related structures. DOM aromaticity and the ratios of HIX to BIX and protein‐like to humic‐like components may be used as a compelling proxy to measure change in source waters and to infer permafrost dynamics. Our results provide insight into the seasonal variation in DOM composition for different size‐fractions in the lower Yukon River, and a baseline dataset against which future changes can be understood in the context of arctic basin biogeochemical cycling.
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 | 0.000 | 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) | 0.000 | 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".