Albedo Trend Analyses in Atlantic Forest Biome Areas
Bibliographic record
Abstract
The albedo is an important variable that controls the balance of radiation and energy of the atmosphere, so changes in land cover cause alterations in albedo values, influencing changes in climate behavior at different scales. The goal in this work was to investigate the possible occurrence and causes associated with surface albedo trends within the Atlantic Forest biome (São Francisco de Paula, state of Rio Grande do Sul, Brazil), during the last thirty years (1987-2017), evaluating the impacts of the forest cover structure on albedo trends. The study included images of the TM/Landsat 5 and OLI/Landsat 8 sensors over the period 1987 to 2017. The surface albedo was obtained from the SEBAL algorithm, which includes in its variables the reflectance values of each band, reflected solar radiation and atmospheric transmissivity. The trend analysis was performed by the Mann-Kendall test verifying the existence of significant trends over 30 years. Subsequently, the influence of vegetation greenness on the trend presented by the albedo surface was evaluated. Approximately 92% of the pixels with significant tendency are associated with the decreasing tendency of the albedo. The downward trend was observed with the change from the field to the forest cover, while increasing trends were influenced by the change in forest cover, such as the suppression of individuals from the upper forest canopy. The forest populations in areas of the Mata Atlântica biome had a large participation in the energy balance, which exposed a reduction of approximately 60% of the surface albedo with its implantation, showing its importance for reducing the emission of energy to the atmosphere. The spatial pattern of the trend distribution of the surface albedo is related to the concentration and vigor of the arboreal 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 | 0.002 | 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".