Dissolved organic matter quantity and quality in Lake Simcoe compared to two other large lakes in southern Ontario
Bibliographic record
Abstract
Dissolved organic matter (DOM) is a critical component in ecosystem processes and is the largest pool of organic carbon (C) in aquatic environments. In this study, we investigated the variability in quantity and quality of DOM in 3 large lakes in southern Ontario. Water quality parameters were coupled with excitation emission fluorescence spectroscopy and absorption spectra to characterize the DOM and investigate the overarching factors controlling DOM dynamics. The results show that Lake Simcoe has higher dissolved organic carbon (DOC) concentrations than lakes Erie, Ontario, and Hamilton Harbour (an embayment in western Lake Ontario) and suggest that a DOM source independent of watershed inputs is likely an important contributor to the DOC in this system. Five components were identified through parallel factor analysis (PARAFAC), representative of both terrestrial and microbial origin. Their relative intensities in the 4 Lake Simcoe end-members allowed the identification of dominant DOM sources in our studied ecosystems. Lake Simcoe seems to have a similar contribution of agriculturally derived DOM to lakes Erie, Ontario, and Hamilton Harbour. Lake Ontario, including Hamilton Harbour had on average a larger input of DOM derived from wastewater treatment plant effluents. The seasonal patterns in the different optical characteristics of DOM in Lake Simcoe compared to other systems suggested that DOM qualitative transformations, be it through photooxidation or microbial degradation, are likely very important processes in this lake. The role of DOM in Lake Simcoe may have important ecological implications for the cycling of C and the oxygen regime of this lake.
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 | 0.001 |
| 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".