Impacts of climatic variability on surface water area observed by remotely sensed imageries in the Red River Basin
Bibliographic record
Abstract
Recent wetting in the Northern Great Plain (NGP) exerted strong influences on lakes and wetlands. However, the influence of recent increase in precipitation on spatiotemporal variation of surface water area is poorly understood in the Red River Basin (RRB, northern United States and southern Canada). Here, we used a high-resolution global surface water dataset to understand spatiotemporal dynamics of the annual, total, permanent, and seasonal water extent in RRB. Monthly surface water area is investigated to detect the change in seasonal surface water extent. We found four distinct phases of variation in surface water: Phase 1 (1990–2001, wetting); Phase 2 (2002- 2005, dry); Phase 3 (2006–2013, recent wetting); and Phase 4 (2014–2019, recent drying). A bare land to a permanent and seasonal water area switch is observed during Phase 1, while the other phases have experienced relatively little fluctuation. Findings have implications for nutrient concentration assessment in lakes and wetlands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".