Changes in past global solar radiation based on climate models and remote sensing in the state of Rio de Janeiro, Brazil
Bibliographic record
Abstract
The objective of this study was to perform a temporal and spatial analysis of the changes in the past global solar radiation based on climate models and remote sensing data in the State of Rio de Janeiro, Brazil for the baseline period (1961-1990). Data from two climate models - the Canadian Centre for Climate Modelling and Analysis and Geophysical Fluid Dynamic Laboratory, were used for the A1B intermediate scenario, data from Conventional Weather Stations and orbital sensor Moderate Resolution Imaging Spectroradiometer (MODIS) MODIS Land Cover Type (MCD12Q1). The results of the Spatial Dependence Degree indicate that the best model to represent the global solar radiation is CCCMA-Exponential dry 70.01 and rainy 0.21 respectively. It was possible to verify that in places where the areas are occupied by forests there was reduction of global solar radiation for both dry and rainy periods of approximately 13 MJ m-2d-1. These results indicate that these forest areas can function as islands of freshness, while maintaining the thermal comfort balanced. It was concluded that the dry period had the highest values of solar radiation compared to the rainy (57%), which can be justified by the occurrence of fires in Rio de Janeiro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".