Cryptic wetlands: integrating hidden wetlands in regression models of the export of dissolved organic carbon from forested landscapes
Bibliographic record
Abstract
Abstract This study examines the relationship between wetlands hidden beneath the forest canopy (‘cryptic wetlands’) and dissolved organic carbon (DOC) export to streams and lakes in forested ecosystems. In the Turkey Lakes Watershed (TLW), located in the Algoma Highlands of central Ontario, Canada, there is substantial natural variation in average annual DOC export (kgC ha−1 year−1), ranging from 11·4 to 31·5 kgC ha−1 year−1 in catchments with no apparent wetlands. We hypothesized that the natural variation in DOC export was related to cryptic wetlands. Cryptic wetlands were derived manually from geographic coordinates that were surveyed with a differential global positioning system, and automatically from identification of topographic depressions and flat slopes (<1·5° ) within a digital elevation model (DEM) in a geographic information system. For the TLW catchments, which are characterized by shallow soils over bedrock, a significant correlation (r2 ≥ 0·9, p < 0·001) between manual and automated methods was observed for scales up to 50 m when a light detection and ranging DEM was used for the topographic analysis. Regression models indicated that cryptic wetlands (%) explained the majority of the natural variation in DOC export (kgC ha−1 year−1), with r2 = 0·88 (p < 0·001) for the model based on the manually derived wetlands and r2 = 0·85 (p < 0·001) for the model based on the automatically derived wetlands. The strength and significance of the automatically derived wetlands (%) versus DOC export (kgC ha−1 year−1) regression model diminished when other sources of DEMs were used. This study emphasizes the importance of including cryptic wetlands in predictive models of DOC export, particularly in catchments where the topography includes depressions and flat areas but no apparent wetlands. Copyright © 2003 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".