Nutrient Budgets Calculated in Floodwaters Using a Whole‐Ecosystem Reservoir Creation and Flooding Experiment
Bibliographic record
Abstract
Abstract Flooding in forested areas can release nutrients via organic matter flushing and organic matter decomposition and contribute to nutrient loading in aquatic ecosystems. Organic matter content may set an upper limit for nutrient release during flooding, but variation in timing, depth, duration of inundation, and site organic matter substrate inhibit comparison across sites and events. We used data collected during a whole‐ecosystem flooding experiment conducted at the IISD‐Experimental Lakes Area (ELA) in northwestern Ontario to examine the relationship between site organic matter content and nutrient release on both a short‐term (first 3 weeks) and annual scale (open‐water season). Three upland forest sites with differing amounts of organic matter stored in soil and vegetation were flooded to create experimental reservoirs. We hypothesized that the magnitude of nutrient release would be related to the organic matter content in each site. We found that nutrient concentrations increased relative to low‐nutrient water pumped into each reservoir at both scales, but there was no relationship between site organic matter content and short‐term total nitrogen (TN) and total phosphorus (TP) concentrations or annual TN and TP fluxes. Additionally, N and P release rates differed, decreasing TN:TP ratios in reservoirs and outflows relative to inflows. Nutrient concentrations increased immediately after inundation each year and continued for 5 years of repeated flooding. Predicted increases in flooding due to changing precipitation patterns and reservoir creation may cause forested areas to be a long‐term nutrient source to aquatic ecosystems. Over time, nutrient accumulation may affect water quality, shift biological communities, and influence ecosystem functioning.
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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 | 0.000 | 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 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".