Bibliographic record
Abstract
Governments, corporations and individuals all need to take immediate action to help change the global economy toward a circular economy. A circular economy which uses fewer resources and based on renewable clean technologies to help limit global warming to 1.5 °C. The 2018 Intergovernmental Panel on Climate Change (IPCC) report warned that limiting global warming to 1.5 °C above pre-industrial levels would require current greenhouse-gas (GHG) emissions to be cut in half by 2030. Yet actions by governments, corporations and individuals are lagging behind. Many countries are failing their obligations made under the 2015 Paris climate agreement. Even the International Maritime Organization, a United Nations agency set a 50% reduction target of GHG emissions for global shipping by 2050, but this falls short of the IPCC target by 20 years. The United Nations climate summit in New York this week (September 2019) needs to send a strong wake up call to the entire world for us all to change. Change makers like Greta Thunberg has already done that. Individual actions to change consumer behaviour can play a major role to help reduce GHG emissions. Even reducing use of single-use plastics (a petroleum derivative) and incineration can help reduce GHG emissions. GHG emissions from plastics could reach 15% of the global carbon budget by 2050 if not curbed. In Europe, plastic production and incineration emits an estimated ~400 million tonnes of CO 2 per year. Therefore, reducing single-use plastic use could curb GHG emissions.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.218 | 0.153 |
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".