Distributed topographic indicators for predicting nitrogen export from headwater catchments
Bibliographic record
Abstract
The possibility of using topographic indicators to predict spatial variation in dissolved nitrogen (N) export from headwater catchments was explored within a sugar maple forest in the Algoma Highlands of central Ontario, Canada, where the average annual export of total dissolved N export ranged from 3.58 to 6.96 kg N ha −1 a −1 . Topographic indicators representing both “nondistributed” and “distributed” properties of the catchments were derived. Distributed topographic indicators that were designed to represent hydrologic flushing mechanism for N export were superior in predicting nitrate‐N export, explaining up to 85% in average annual nitrate‐N export and 90% in the slope of discharge versus peak nitrate‐N export which occurred during spring melt. However, the distributed topographic indicators were comparable to nondistributed ones for dissolved organic nitrogen export, explaining up to 68% of the variance compared to 65%. This study shows that spatial variation in N export from catchments within a relatively small region can be substantial, but that distributed topographic indicators can be used to predict a majority of this N export and thereby provide a basis for extrapolating N export from a few intensively monitored catchments to many other catchments within the sugar maple forest of the Algoma Highlands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".