SUSTENTABILIDADE DE DATA CENTERS COM O USO DA TI-VERDE
Bibliographic record
Abstract
Os Data Centers são grandes consumidores de energia elétrica, causando impactosao meio ambiente durante o seu ciclo de vida. O objetivo deste trabalho é apresentarvantagens tecnológicas com foco ambiental através da TI-Verde para asustentabilidade dos Data Centers. Estes centros de processamentos de dadoscomputacionais são responsáveis por parte de emissão de gases de efeito estufarelacionados ao processo de geração de energia. Neste aspecto, a TI-Verde surgecom uma nova modalidade de conhecimentos, onde foi possível perceber por meiodeste trabalho que sua aplicação traz reais retornos em eficiência energética. Foiapresentado um estudo de caso que tratou dados de 3 grandes empresas queaplicaram conceitos e atitudes de TI-Verde em seus Data Centers. Concretizandovantagens voltadas a diminuição de consumo de energia, e tornando estes centrostecnológicos ecologicamente corretos, tais práticas de tecnologia verde nãopretendem diminuir a intensidade do uso da informação, mas mostrar como as novastecnologias poderão ajudar a diminuir seu impacto ambiental.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".