Legacy Effects Following Fire on Surface Energy, Water and Carbon Fluxes in Mature Amazonian Forests
Bibliographic record
Abstract
Abstract The ongoing deforestation process in Amazonia has led to intensified forest fires in the region, particularly in Brazil, after more than a decade of effective forest conservation policy. This study aims to investigate the recovery of two mature sub‐montane ombrophile Amazonian forests affected by fire in terms of energy, water and carbon fluxes utilizing remote sensing (MODIS) and climate reanalysis data (GLDAS). These two forest plots, mainly composed of Manilkara spp. (Maçaranduba), Protium spp. (Breu) (∼30 m), Bertholletia excelsa (Castanheira) and Dinizia excelsa Ducke (Angelim‐Pedra) (∼50 m), occupy areas of 100.5 and 122.1 km2 and were subject to fire on the same day, on September 12, 2010. The fire significantly increased land surface temperature (0.8°C) and air temperature (1.2°C) in the forests over a 3 years interval. However, the forests showed an ability to recover their original states in terms of coupling between the carbon and water cycles comparing the 3‐year periods before and after the fires. Results from a wavelet analysis showed an intensification in annual and seasonal fluctuations, and in some cases (e.g., daily net radiation and evapotrasnspiration) sub‐annual fluctuation. We interpreted these changes to be consistent with overall intensification of the coupling of energy balance components and drivers imposed by climate and solar cycle seasonality, as well as faster time scale changes, consistent with a shift toward greater forest openness and consequent reduction in the interception of incoming solar radiation by the canopy.
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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 | 0.000 |
| 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".