Nutrient export from catchments on forested landscapes reveals complex nonstationary and stationary climate signals
Bibliographic record
Abstract
[1] Headwater catchment hydrology and biogeochemistry are influenced by climate, including linear trends (nonstationary signals) and climate oscillations (stationary signals). We used an analytical framework to detect nonstationary and stationary signals in yearly time series of nutrient export [dissolved organic carbon (DOC), dissolved organic nitrogen (DON), nitrate (NO3−-N), and total dissolved phosphorus (TDP)] in forested headwater catchments with differential water loading and water storage potential at the Turkey Lakes Watershed in Ontario, Canada. We tested the hypotheses that (1) climate has nonstationary and stationary effects on nutrient export, the combination of which explains most of the variation in nutrient export; (2) more metabolically active nutrients (e.g., DON, NO3−-N, and TDP) are more sensitive to these signals; and (3) catchments with relatively low water loading and water storage capacity are more sensitive to these signals. Both nonstationary and stationary signals were identified, and the combination of both explained the majority of the variation in nutrient export data. More variation was explained in more labile nutrients (DON, NO3−-N, and TDP), which were also more sensitive to climate signals. The catchment with low-water storage potential and low water loading was most sensitive to nonstationary and stationary climatic oscillations, suggesting that these hydrologic features are characteristic of the most effective sentinels of climate change. The observed complex links between climate change, climatic oscillations, and water nutrient fluxes in headwater catchments suggest that climate may have considerable influence on the productivity and biodiversity of surface waters, in addition to other drivers such as atmospheric pollution.
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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 | 0.001 |
| 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".