Successfully Integrating the Challenge of Global Climate Change with Business Strategy
Bibliographic record
Abstract
Abstract Shell Canada Limited (SCL) shares the concern being expressed globally over the issue of climate change. We accept that there is sufficient evidence of a human impact on the climate system to support taking action on climate change. Shell People will continue to participate with governments and other sectors of society, in the policy debate to develop and implement responsible actions that contribute to emissions reductions and also protect Canada's interests. Climate change is a long-term issue. The economic and related lifestyle costs of emissions mitigation policies can be reduced if they are phased in over time. Shell Canada believes that a measured approach provides time for better scientific understanding of the climate system, for the development of new and turnover of old technologies, and for the development of less carbon-intensive fuels while allowing the economy and society time to adjust. Our business strategy includes a vision for the future and actions to manage our role on climate change today. Thus, Shell Canada is committed to taking action on climate change and will strive to continuously improve the energy efficiency performance of existing and new businesses to reduce overall greenhouse gas (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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.030 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".