Toward artificial intelligence and machine learning-enabled frameworks for improved predictions of lifecycle environmental impacts of functional materials and devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The application of functional materials and devices (FM&Ds) underpins numerous products and services, facilitating improved quality of life, but also constitutes a huge environmental burden on the natural ecosystem, prompting the need to quantify their value-chain impact using the bottom-up life cycle assessment (LCA) framework. As the volume of FM&Ds manufactured increases, the LCA calculation speed is constrained due to the time-consuming nature of data collection and processing. Moreover, the bottom-up LCA framework is limited in scope, being typically static or retrospective, and laced with data gap challenges, resulting in the use of proxy values, thus limiting the relevance, accuracy, and quality of results. In this prospective article, we explore how these challenges across all phases of the bottom-up LCA framework can be overcome by harnessing new insights garnered from computationally guided parameterized models enabled by artificial intelligence (AI) methods, such as machine learning (ML), applicable to all products in general and specifically to FM&Ds, for which adoption remains underexplored. Graphical abstract
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it