Characterizing hydrodynamics on boreal landscapes using archived synthetic aperture radar imagery
Bibliographic record
Abstract
Abstract Characterizing the spatial and temporal variation in surface hydrological dynamics of large boreal landscapes is vital, since these patterns define the occurrence of key areas of land‐to‐lake and land‐to‐atmosphere hydrological and biogeochemical linkages that are critical in the movement of matter and energy at local to global scales. However, monitoring surface hydrological dynamics over large geographic extents and over long periods of time is a challenge for hydrologists, as traditional point measurements are not practical. In this study we used European Remote Sensing satellite radar imagery to monitor the variation in surface hydrological patterns over a 12‐year period and to assess the change in the organization of saturated and inundated areas of the landscape. Using the regional Utikuma River drainage basin (2900 km 2 ) as the test area, the analyses of patterns of wetlands indicated that, during dry climatic conditions, wetland sizes were small and disconnected from each other and receiving bodies of water. As climatic conditions changed from dry to mesic, wetland numbers increased but were still disconnected. Very wet climatic conditions were required before the disconnected wetlands coalesced and connected to lakes. During these wet conditions, the response of the lake level at Utikuma Lake was observed to be much higher than under drier conditions. Analyses of individual wetland maps and integrated wetland probability maps have the potential to inform future biogeochemical and ecological investigations and forest management on the Boreal Plain. Copyright © 2007 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".