Methodology for estimating emissions from agriculture in the Netherlands. : Calculations of CH4, NH3, N2O, NOx, PM10, PM2.5 and CO2 with the National Emission Model for Agriculture (NEMA)
Bibliographic record
Abstract
Methodology for estimating emissions from agriculture in the Netherlands.Calculations of CH4, NH3, N2O, NOx, PM10, PM2.5 and CO2 with the National Emission Model for Agriculture (NEMA).Wageningen, The Statutory Research Tasks Unit for Nature and the Environment (WOT Natuur & Milieu).WOt-technical report 53.164 p; 21 Tab.; 1 Fig.; 108 Ref.; 12 Annexes.The National Emission Model for Agriculture (NEMA) is used to calculate emissions to air from agricultural activities in the Netherlands on a national scale.Emissions of ammonia (NH3) and other N-compounds (NOx and N2O) from animal housing, manure storage, manure application and grazing are assessed using a Total Ammoniacal Nitrogen (TAN) flow model.Furthermore, emissions from application of inorganic N-fertilizer, compost and sewage sludge, cultivation of organic soils, crop residues, and ripening of crops are calculated.NEMA is also used to estimate emissions of methane (CH4) from enteric fermentation and manure management, particulate matter (PM) from manure management and agricultural soils, and carbon dioxide (CO2) from liming.Emissions are calculated in accordance with international guidance criteria and reported in an annual Informative Inventory Report (IIR; for air pollutants) and National Inventory Report (NIR; for greenhouse gases).This methodology report describes the outline and backgrounds of the emission calculations with NEMA.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".