Plenary lecture 1: utilization of non-fossil fuel energy options to mitigate climate change and environmental impact
Bibliographic record
Abstract
Non-fossil fuel energy options can help humanity combat climate change and provide the opportunity for sustainable energy solutions. Non-fossil fuel energy options are diverse, ranging from renewables like solar, wind, geothermal, hydropower, biomass, ocean, tidal and wave energy, through to nuclear energy. The latter may not be a renewable resource, but it avoids greenhouse gas emissions and thus contributes to efforts to avoid climate change. Renewable energy resources are normally free of greenhouse gas emissions, although some like biomass can lead to such emissions if not managed carefully. Non-fossil fuel energy options are not sufficient for avoiding climate change, in that they are not necessarily readily utilizable in their natural forms. Hydrogen energy systems are needed to facilitate the use of non-fossil fuels by allowing them to be converted to two main classes of energy carriers: hydrogen and select hydrogen-derived fuels and electricity. The former allow humanity to meet most of its chemical energy needs, while the latter can satisfy most non-chemical energy demands. High efficiency is also needed to allow the greatest benefits to be attained from all energy options, including nonfossil fuel ones, in terms of climate change and other factors. Efficiency improvements efforts have many dimensions, including energy conservation, improved energy management, fuel substitution, better matching of energy carriers and energy demands, and more efficiency utilization of both energy quantity and quality. The latter two concepts are best considered via the use of exergy analysis, a thermodynamic tool based primarily on the second law of thermodynamics. A case study is considered involving the production of hydrogen from non-fossil energy sources via thermochemical water decomposition. This process is mainly driven by thermal energy, and is anticipated to be usable for large-scale hydrogen production. In thermochemical hydrogen production, a series of complex chemical and other processes occur, with the net result being the splitting of water into hydrogen and oxygen. Most preliminary designs of thermochemical hydrogen production processes are based on nuclear energy and solar energy, thus providing different types of non-fossil fuel options for combating climate change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.188 | 0.133 |
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".