Bibliographic record
Abstract
Carbon Credit Trading is a product of the" ... 1998 Kyoto Accords on global warming, which call for reducing worldwide CO2 emissions to below 1990 levels." (Blank, 2001). The plan is a method, the merits, and or lack of merits that shall be examined herein, to arrange for emissions to meet the indicated objective by essentially swapping dirty air, for clean air. Blank (2001) provides an illustration of the preceding as follows: \n"Because some coal-burning utilities lack the technology to reduce emissions to Kyoto levels on their own, they are banking on greenhouse gas trades with farmers, who can "sequester" carbon (absorb it through the land) using such methods as no-till cultivation. That's sent a host of carbon brokers to Iowa, Nebraska and lllinois in search of credit deals. These carbon trades, though engineered by reputable brokerages, are totally unregulated and there are no guarantees that any government will officially recognize the credits. That hasn't prevented several major players in Canada, Australia, New Zealand, United Kingdom and the European Union from signing contracts."\nThe concepts involved in carbon credit trading require some explanation in order to understand the foundation as well as purpose of the plan. The preceding being the case, this examination shall explore the development of this approach to provide the historical as well as functional background from which to equate its benefits, shortcomings, potential, and other points, looking at Ireland as the principle example.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.302 | 0.110 |
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".