Differences in Riverine and Pond Water Dissolved Organic Matter Composition and Sources in Canadian High Arctic Watersheds Affected by Active Layer Detachments
Bibliographic record
Abstract
Regional warming has caused permafrost thermokarst and disturbances, such as active layer detachments (ALDs), which may alter carbon feedback in Arctic ecosystems. However, it is currently unclear how these disturbances alter DOM biogeochemistry in rivers and ponds in Arctic ecosystems. Water samples from the main river channel, ALD-disturbed/undisturbed tributaries, and disturbed/undisturbed ponds within a catchment in the Canadian High Arctic were collected and analyzed using carbon isotopes and spectroscopic methods. Both river and pond samples had large variations in dissolved organic carbon (DOC) concentrations. Ponds, particularly ALD-disturbed ponds, had much older 14 C DOC ages than rivers. Results from δ 13 C and absorption and fluorescence analyses indicate higher autochthonous contributions in ponds than rivers and increasing autochthonous contributions from upper to lower reaches of the main channel. The disturbed samples had less carbohydrates but more carboxyl-rich alicyclic molecules in 1 H nuclear magnetic resonance spectra than undisturbed samples. These ALD-impacted samples also contained less terrestrial-humic-like but more oxidized-quinone-like components in the fluorescence spectra. Interestingly, the disturbed pond DOM displayed the greatest DOM oxidation with ALDs compared to undisturbed areas. Compared to Arctic rivers, small Arctic ponds have DOM predominantly from permafrost and microbial sources and may have a disproportionally stronger positive feedback on climate warming.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".