Application of machine learning in technological forecasting
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.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".