Distinct Optical Chemistry of Dissolved Organic Matter in Urban Pond Ecosystems
Bibliographic record
Abstract
Urbanization has the potential to dramatically alter the biogeochemistry of receiving freshwater ecosystems. We examined the optical chemistry of dissolved organic matter (DOM) in forty-five urban ponds across southern Ontario, Canada to examine whether optical characteristics in these relatively new ecosystems are distinct from other freshwater systems. Dissolved organic carbon (DOC) concentrations ranged from 2 to 16 mg C L(-1) across the ponds with an average value of 5.3 mg C L(-1). Excitation-emission matrix (EEM) spectroscopy and parallel factor analysis (PARAFAC) modelling showed urban pond DOM to be characterized by microbial-like and, less importantly, by terrestrial derived humic-like components. The relatively transparent, non-humic DOM in urban ponds was more similar to that found in open water, lake ecosystems than to rivers or wetlands. After irradiation equivalent to 1.7 days of natural solar radiation, DOC concentrations, on average, decreased by 38% and UV absorbance decreased by 25%. Irradiation decreased the relative abundances of terrestrial humic-like components and increased protein-like aspects of the DOM pool. These findings suggest that high internal production and/or prolonged exposure to sunlight exerts a distinct and significant influence on the chemistry of urban pond DOM, which likely reduces its chemical similarity with upstream sources. These properties of urban pond DOM may alter its biogeochemical role in these relatively novel aquatic ecosystems.
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 | 0.001 |
| 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".