Wetland seasonal dynamics and interannual variability over northern high latitudes, derived from microwave satellite data
Bibliographic record
Abstract
A new accurate determination of the spatial and temporal variations of northern wetland area extent, during the snow‐free period, is retrieved from the 19‐ and 37‐GHz passive microwave Special Sensor Microwave/Imager (SSM/I) brightness temperature database. The total fraction of water surface (FWS) area, corresponding to small lakes or reservoirs (area < 25 × 25 km2), inundated surfaces and natural wetland areas, computed on a weekly basis and averaged over the 1988–2001 period, gives a maximum summer extent of 10.10 ± 0.21 × 105 km2 for Canada and Alaska and 12.7 ± 0.35 × 105 km2 for Eurasia, above 50°N. The mean seasonal dynamics of wetland and inundated surfaces shows a maximum area extent during the months of July‐August‐September over North America (48°N–68°N) and between June and September over northern Eurasia. The interannual variations of the yearly total FWS area since 1988 show a slight increase (8% ± 3%) in Canada‐Alaska, mainly due to a decrease in the snow cover extent. The FWS interannual variability in the Ob River basin, which is well explained by the water discharge variations, shows an increase of 8.5% ± 1.5% between 1988 and 2001. The microwave‐derived FWS estimate and its dynamics present an interesting potential for studying the climate‐driven variability of wetlands and inundated areas.
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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 | 0.000 |
| 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 | 0.000 | 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 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".