Greenhouse gas emissions in the Netherlands 1990 -\n1995. Methodology and data for 1994 and provisional data for 1995
Bibliographic record
Abstract
The inventory presented in this report complies with the obligations under the European Union's Greenhouse Gas Monitoring Mechanism and the UN-FCCC for emission reports on all greenhouse gases not covered under the Montreal protocol. This inventory of greenhouse gas emissions in the Netherlands has been prepared according to the IPCC Guidelines on the basis of figures provided by the Environmental Balance 1996. A short description is given on how the Guidelines have been applied in the Netherlands. Differences between IPCC sectors and target groups in the Netherlands are addressed and resulting emission differences accounted for. Time series on emissions between 1990 and 1995 are presented for all non-ODP greenhouse gases. In 1994, carbon dioxide emissions were 2.8% higher than in 1990. In the period 1990-1994 methane emissions decreased by 3%, nitrous oxide emissions increased by 11% and CO2-equivalent emissions of HFCs, PFCs, and SF6 increased by 17%. The total emissions of non-ODP greenhouse gases increased by 3% from 1990 to 1994, mainly due to increasing emissions of CO2 and HFCs. In 1994, non-CO2 gases contributed about 22% to all CO2-equivalent emissions in that year. CH4 contributed about 10%, N2O about 8% and the non-ODP halocarbons about 4%. Provisional data suggest an increase of 3.9% carbon dioxide emissions in 1995 due to increased energy use. The emissions of HFCs were 33% higher in 1995 than in 1994.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".