Cycles in hydrologic intensification and de-intensification create instabilities in spring nitrate-N export C-Q behavior in northern temperate forests
Bibliographic record
Abstract
Northern temperate forests are experiencing changes from climate and acidification recovery that influence catchment nitrate-nitrogen (N) flushing behavior. N flushing behavior is characterized by metrics such as: (a) N flushing time—the exponential decrease in stream N concentration during the peak snowmelt episode; and (b) N concentration (C) and discharge (Q) hysteresis metrics—flushing index (FI) and hysteresis index (HI)—representing the slope, direction and amplitude of the C-Q loop. We hypothesize that climate-driven hydrologic intensification results in longer N flushing times, lower FI (less flushing to more diluting), and lower HI (less proximal to more distal N sources). We tested this hypothesis using 38 years of data from two headwater catchments. Hydrologic intensification was estimated by changes in the ratio of potential evapotranspiration to precipitation and the ratio of actual evapotranspiration to precipitation. From 1982 to 2005, a period of hydrologic intensification and a decline in atmospheric acidic deposition was associated with a decrease in C and Q, leading to stable C-Q patterns that reflected flushing (positive FI) of proximal N sources (positive HI). However, from 2006 to 2019, a period of hydrologic de-intensification coupled with an ongoing decline in atmospheric acidic deposition was associated with a continued decrease in C but an increase in Q, leading to unstable C-Q patterns that reflected diluting (negative FI) of distal N sources (negative HI). C-Q instability was buffered in the catchment with a large wetland, indicating the potential of wetlands to buffer against changing climate conditions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".