It-Technologies In The Implementation Of Climate Projects
Bibliographic record
Abstract
The global market for carbon units began to take shape during the first period of the Kyoto Protocol (2008–2012). In 2015, a new global climate treaty, the Paris Agreement, was approved, the economic mechanisms of which are still being developed. So far, a number of regional schemes operate in the world, including in the EU, a number of provinces in China, a number of US states and Canada (usually in the form of quota systems and carbon markets), and voluntary schemes. The article discusses IT technologies in the implementation of climate projects. Modern digital technologies are developing very quickly and are present in all business sectors. It is IT that helps companies to make the transition to a model of advanced harmless production, which means the use of safe materials, intelligent systems, etc. The essence of the concept is a description of approaches to the implementation of projects to reduce greenhouse gas emissions or increase the absorption capacity of ecosystems by Russian companies. Information technology is a set of methods and means of purposefully changing any properties of information. Information technology in the field of management makes the highest demands on the "human factor", having a fundamental impact on the qualifications of the employee. Information technology is an important component of the process of using information resources..
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".