Estimate of net primary production of aquatic vegetation of the amazon floodplain using SAR satellite data
Bibliographic record
Abstract
2 Instituto Nacional de Pesquisas Espaciais (INPE), Brazil Abstract-Field measurements were combined with synthetic aperture radar images to evaluate the use of RADARSAT and JERS-1 for estimating biomass changes and mapping of aquatic vegetation, and subsequently estimating of net primary productivity of aquatic vegetation in the lower Amazon. The combination of C and L bands provides the best correlation (r =0.82) and an intermediate saturation point (620 gm -2 ) for estimating above water biomass of aquatic vegetation. A combination of RADARSAT and JERS-1 images from each water period was classified using a region growing algorithm, and yielded an accuracy higher than 95% for the seasonal vegetated areas of the floodplain. The combination of the seasonal mapped area of aquatic vegetation with the statistical SAR-algorithm for estimating above water biomass and the percentage of below water biomass yielded a total annual NPP of 1.9x10 12 g C yr -1 (±28%) for aquatic vegetation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".