Application of machine learning in technological forecasting
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
The plastics industry is vital to Canada's economy, particularly in Quebec. However, environmental challenges persist, prompting companies to invest in research to enhance product performance and sustainability. Recent developments include biodegradable polymers and composite materials. This research aims to develop an automated method for extracting and analyzing text data through text similarity analysis and LDA (Latent Dirichlet Allocation) topic modeling. This approach helps identify both existing and emerging patented innovations, creating new categories within the patent classification system. The RoBERTa model, based on BERT and trained on patent data, has proven highly effective in identifying semantic similarities between technological classes and their patent summaries, achieving an accuracy significantly greater than 80%, regardless of the similarity threshold. The LDA topic analysis showed a 52% topic consistency score. A review of academic publication summaries from the Web of Science database revealed, for example, transitional approaches to the circular economy. These approaches represent a promising option for managing the end-of-life of plastics while reducing environmental pollution.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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