How do hurricanes impact forest ecosystems?
Bibliographic record
Abstract
Climate change is creating unprecedented risks for businesses around the world, which is making investors nervous.A key way that businesses can address this anxiety is through disclosing how they impact, and are impacted by, climate change.Dr Sanjay Banerjee of the University of Alberta in Canada is researching how businesses are going about this, and what this means for future policy and regulation.How is climate change affecting accounting and business?Accounting research Capital -wealth in the form of money or assets Carbon tax -a tax on the usage of fossil fuels and/or greenhouse gas emissions Climate disclosure -the sharing or publication of climaterelated information related to a business, such as climate risk assessments and mitigation plans Climate risk -the potential for the impacts of climate change to have negative consequences Investor -a person or organisation that gives money towards a certain business or project, with the expectation of receiving a profit from the investment at a later date Machine learning -a type of artificial intelligence that uses algorithms to imitate the way that humans learn and perform certain tasks more accurately over time Shareholder -any person or organisation that owns shares in a company Shares -the subunits of a company's capital/stock Stock -the sum of a company's shares U nderstanding how companies are responding to the threat of climate change is of huge interest to investors, regulators and policy makers.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".