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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".