Bibliographic record
Abstract
Renewable energies such as solar and wind are now widely accepted as possible future energy sources to enhance the energy demands and support the intense desire to reduce the worldwide carbon footprint. Over the last few years, renewable energy sources have won the legislative support of governments in several countries. The main reason for this boom is the need to use alternative energy sources, to fossil fuel, which are free of CO2 emissions and contamination. Unfortunately there are no renewable energy sources that can currently account for zero carbon footprints due to the fact that it is divided into two elements direct and indirect. The direct or primary carbon footprint is related to the CO2 emitted when burning fossil fuels in such areas as transportation and domestic energy consumption. As well as the direct carbon footprint which is universally accepted, there is also an indirect or secondary element which is associated with the life cycle of the products. The problem is how can this indirect carbon footprint be considered or included when discussing the total impact of renewable energy sources. Life Cycle Assessment is a method used to identify the main types of impact on the environment a wind turbine can cause throughout its 20 year life of operation. Emissions produced while wind turbines are in operation in addition to factors from the start of the manufacturing process to the last stage where dismantling is required at end-of-life. Life cycle assessment showed that wind turbines are not carbon neutral as they have an indirect carbon footprint. However, when compared with traditional energy sources the carbon footprint for non-traditional energy forms is still significantly limited.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.004 |
| 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 teacher head, 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".