Avaliação do ciclo de vida de ferrovias de carga: uma análise crítica e uma proposta de diretriz
Bibliographic record
Abstract
Este estudo realiza uma revisão de vários artigos no contexto de ferrovias, em que a metodologia de avaliação do ciclo de vida (LCA) é aplicada. Usando a metodologia ProKnow-C, foram analisados sete artigos de revisão e 85 artigos originais que aplicam a metodologia de ACV a ferrovias, e foram identificadas várias lacunas importantes, principalmente no que diz respeito à falta de divulgação de informações sobre parâmetros, software e outras decisões importantes tomadas durante os trabalhos que permitiriam a outros pesquisadores replicar os resultados para compará-los com outras ferrovias ou circunstâncias diferentes. Atualmente, não há normas ISO que abordem a ACV de ferrovias, portanto, este trabalho busca fornecer um conjunto inicial de diretrizes, facilitando assim a elaboração de tal norma e fornecendo suporte e orientação para pesquisadores da área.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".