Bibliographic record
Abstract
Innovating polymers: 100% recycled ECOPLASTOMER® Katarzyna Pokwicka-Croucher, Founder and CEO of Ecopolplast, tells us about the company’s mission to innovate polymers through their eco-friendly Ecoplastomer® product, made with 100% recycled content that reduces CO2 emissions and ensures complete independence from virgin raw materials. Global production of polyolefins has increased by over 1000% since the 1950s. The amount of municipal waste based on these polymers each year is 2.1 billion tonnes. This is the amount of only one selected group of materials. Considering other types of polymers such as PET, PS, PA, PC, or cross-linked materials, i.e., XLPE and rubber, this amount will be several dozen times higher. This problem, along with environmental regulations, has increased consumer awareness and producers’ responsibility for plastic recycling. Certain laws and regulations (the European Union’s REACH (EC) No. 1907/2006, the United Nations’ Paris Agreement, and Canada’s carbon pricing policy (the ‘carbon tax’)) help to significantly mitigate the environmental impact of waste. A study of the EU market on a global production scale showed that the countries with higher conversion were Germany, Italy, France, Spain, Great Britain, and Poland (starting with the largest producers), which produced ~80% of plastic waste. Moreover, the depletion and the slow disappearance of fossil fuels, which are the raw material for the production of plastics worldwide, emphasize the need to find uses for the collected waste that will be recycled.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".