Hoeveel CO2 stoten ministers uit?
Bibliographic record
Abstract
October 10th will be the big “March for climate” in Brussels. Politicians are summoned to solve the problem. I apply the principles of free thinking analysis, and ask how much greenhouse gas is produced by the members of the government? No data are available. For what their cars exhaust, the car manufacturer is responsible. Scientists showed that cows produce a lot of methane, which is 80 times more active than CO2. In fact bacteria in their intestine are responsible. What can the government do about that? Steel factories exhaust nearly 2 tons of CO2 for each ton of steel, worldwide; in Belgium they produce 8% of the CO2 exhaust. But the family Mittal has now obtained financial support from the Flemish, Belgian and Canadian governments, and from Europe, for their plans to replace coal by hydrogen ("green steel"). At the same time Arcelor-Mittal is returning 4.3 billion to its stockholders. These examples show that the climate problem can be solved when governments open their purse.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".