3D Models for web based climate education 
Bibliographic record
Abstract
Effectively informing the public about anthropogenically accelerated climate change and sustainable energy is one of the most immense challenges of our age. However, web-based 3D environments are cost-effective, accessible tools that can combat many of the challenges associated with global outreach, especially during the COVID-19 pandemic. This presentation explores how 3D CAD modeling, visual texturing, Three.JS (a WebGL rendering software), and web design can coexist to create effective tools for educators across the world. By applying these simulations, learners are able to examine individual components of objects and break down complex systems into their fundamental parts for simpler understanding. Moreover, by breaking down these systems, individuals are able to more effectively understand the complex physical phenomena that drive our world. In addition, these environments are not limited by topic or language and therefore the spectrum to which we can apply these ideas is not limited. Translating the simulations is relatively straightforward and with the expertise of individuals who can lead this front, the reach of this type of technology can grow even wider. Climate change is a global issue and so work in the field must be addressed as such as well. As a result, these models have the ability to transform the way we learn about global issues and can be a powerful tool in education about sustainable energy, climate change and science in general.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.023 |
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".