Long-term trend and drivers of inter-annual variability of surface water dissolved organic carbon concentration in a forested watershed
Why this work is in the frame
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Bibliographic record
Abstract
Dissolved organic carbon (DOC) concentrations have increased over the past few decades in surface waters across Europe and North America. This has drawn a lot of attention, given the key role of DOC in the global carbon cycle and in surface water biogeochemistry and ecology. While many reports have focused on DOC response to environmental changes in headwater streams and lakes taken separately, there is a lack of studies that combines streams and lakes with varying catchment characteristics in a network-scale perspective. Here, long-term (1987–2018) trends were analyzed and environmental drivers of year-to-year variations in DOC concentrations were examined in headwater streams, lakes and lake outflows at the Turkey Lakes Watershed (TLW) in Ontario, Canada. Results indicated significant increasing of DOC trends in ten out of 12 headwater streams and in four out of 12 lakes and lake outflows over the study period. In addition, piecewise regression analysis detected breakpoints in the 2000 s for DOC time series data in some stations. Multivariate analysis showed that variations in hydro-climatic conditions and the chemistry of atmospheric precipitations explained 13 % to 99 % of year-to-year variations in DOC concentrations. Air temperature emerged as the most influential factor for lakes and lake outflows while precipitation chemistry was the main driver of inter-annual DOC variation in headwater streams. For the latter, the rate of DOC increase and the proportion of explained variance were mainly dependent on catchment characteristics, notably wetland cover which was related to mean catchment slope and total relief. In the context of global change, further research is needed to better understand how changes in climate and atmospheric deposition may be modulated by catchment attributes and ecosystem types for determining future DOC fate and behaviour in surface waters.
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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) | 0.000 | 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 it