Organic matter cycling in a model restored wetland receiving complex effluent
Bibliographic record
Abstract
Abstract Wetlands have been used to treat anthropogenic effluents for decades due to their intense biogeochemical processes that transform and uptake nutrients, organic matter, and toxins. Despite these known functions, we lack generalizable knowledge of effluent-derived dissolved organic matter (DOM) cycling in wetlands. Here, we quantify the cycling of DOM in one of Canada’s more economically important wetland complexes (Frank Lake, Alberta), restored to hydrologic permanence in the 1980s using urban and agro-industrial effluents. Optical analyses and PARAFAC (parallel factor analysis) modelling showed a clear compositional change from more bioavailable and protein-like DOM at effluent input sites to more aromatic and humic-like at the wetland outflow, likely due to DOM processing and inputs from marsh plants and wetland soils. Microbial incubations showed that effluent DOM was rapidly consumed, with the half-life of DOM increasing from as low as 35 days for effluent, to 462 days at the outflow, as a function of compositional shifts toward aromatic, humic-like material. Long-term averaged dissolved organic carbon (DOC) export was low compared to many wetlands (10.3 ± 2.0 g C m −2 yr −1 ). Consistent with predictions based on water residence time, our mass balance showed Frank Lake was a net source of DOM across all measured years, but shifted from a source to sink among wet and drought years that respectively shortened or lengthened the water residence and DOM processing times. Overall, Frank Lake processes and transforms effluent DOM, despite being a longer-term net source of DOM to downstream environments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.012 | 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 teacher head, 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".