Bibliographic record
Abstract
Abstract An increasing number of organizations and governments are responding to the challenges of climate change by introducing programs to report and, ultimately, reduce greenhouse gas (GHG) emissions. Some programs are voluntary and focus solely on GHG reporting or provide a platform for GHG reduction and removal projects, while others are mandatory and require certain types of facilities or industries to report. Taking a proactive leadership position on climate change is becoming an essential component of good corporate social responsibility, especially as businesses prepare themselves for a future regulated carbon environment. Many institutional investors are requesting carbon footprint data and using the information in assessing the risk of investments in sectors or companies. Green procurement is also becoming more and more commonplace across the supply chain as organizations start to direct expenditures toward suppliers that are taking proactive steps to reduce their carbon footprint. Many businesses recognize that reducing GHG emissions is a long-term commitment and are choosing to take immediate responsibility for their emissions by balancing them with the purchase of an equivalent amount of credible carbon offsets. However, the success of any carbon reporting or reduction programs, whether mandatory or voluntary, will depend heavily on the accuracy and transparency of the reported data, especially as new standards and best practices are adopted. One of the major challenges in carbon accounting stem, in part, from the wide array of GHG quantification and verification standards, protocols and methodologies that a professional must be familiar with.
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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".