Searching for similarity in topographic controls on carbon, nitrogen and phosphorus export from forested headwater catchments
Bibliographic record
Abstract
Topography influences hydrological processes that in turn affect biogeochemical export to surface water on forested landscapes. The differences in long-term average annual dissolved organic carbon (DOC), organic and inorganic nitrogen [NO3−-N, dissolved organic nitrogen (DON)], and phosphorus (total dissolved phosphorus, TDP) export from catchments in the Algoma Highlands of Ontario, Canada, with similar climate, geology, forest and soil were established. Topographic indicators were designed to represent topographically regulated hydrological processes that influence nutrient export, including (1) hydrological storage potential (i.e. effects of topographic flats/depressions on water storage) and (2) hydrological flushing potential (i.e. effects of topographic slopes on potential for variable source area to expand and tap into previously untapped areas). Variations in NO3−-N export among catchments could be explained by indicators representing both hydrological flushing potential (91%, p < 0.001) and hydrological storage potential (65%, p < 0.001), suggesting the importance of hydrological flushing in regulating NO3−-N export as well as surface saturated areas in intercepting NO3−-N-loaded runoff. In contrast, hydrological storage potential explained the majority of variations among catchments in DON (69%, p < 0.001), DOC (94%, p < 0.001) and TDP (82%, p < 0.001) export. The lower explanatory power of DON (about 15% less) compared with that of DOC and TDP suggests another mechanism influencing N export, such as controls related to alternative fates of nitrogen (e.g. as gas). This study shows that simple topographic indicators can be used to track nutrient sources, sinks and their transport and export to surface water from catchments on forest landscapes. Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".