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Record W3158896506 · doi:10.18174/544296

Emissies naar lucht uit de landbouw berekend met NEMA voor 1990-2019

2021· report· nl· W3158896506 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsImpact
Fundersnot available
KeywordsLimeEnvironmental scienceManureParticulatesNitrous oxideManure managementCarbon dioxideAnimal scienceLivestockMethaneEnvironmental engineeringAgronomyChemistryForestryGeographyBiology

Abstract

fetched live from OpenAlex

In the Netherlands, agricultural activities are a major source of gaseous emissions of ammonia (NH3), nitrogen oxide (NO), nitrous oxide (N2O), methane (CH4), non-methane volatile organic compounds (NMVOC), carbon dioxide (CO2) from lime fertilisers and urea fertiliser, and particulate matter (PM10 and PM2.5). The emissions were calculated using the National Emission Model for Agriculture (NEMA). In 2019, NH3 emissions from livestock manure, fertiliser and other sources on farms and hobby farms, from private use and from manure application in terrestrial ecosystems amounted to 112.0 million kg NH3, 6.2 million kg less than in 2018. This decrease was due mainly to the reduction in the size of the dairy herd. Emissions of N2O in 2019 were 18.8 million kg, 0.6 million kg less than in 2018. Emissions of NO in 2019 amounted to 21.7 million kg, 0.7 million kg less than in 2018. Emissions of CH4 decreased from 484 to 480 million kg due to the smaller dairy herd. Emissions of NMVOC amounted to 87.8 million kg in 2019, down from 89.6 million kg in 2018. Emissions of particulate matter PM10 decreased from 5.9 in 2018 to 5.4 million kg in 2019 and PM2.5 emissions decreased from 0.6 to 0.5 million kg. Emissions of CO2 from lime fertilisers and urea decreased from 83.1 to 80.1 million kg. Based on new data for several factors which are described in this report, emission figures have been updated for a number of years in the time series since 1990. Emissions of NH3 from livestock manure have fallen by two-thirds since 1990, mainly as a result of lower nitrogen excretion rates of livestock and the introduction of low-emission manure application. Emissions of N2O and NO decreased over this period by 42% and 35% respectively, less markedly than the NH3 reduction because of higher emissions from manure injection (compared with surface spreading manure) and a shift from excretion on pasture to excretion in animal houses. Emissions of CH4 decreased by 18% between 1990 and 2019 due to a decrease in livestock numbers and increased feed use efficiency of dairy cattle. Emissions of PM10 increased by 9% in the same period due to laying poultry farms switching from housing systems with liquid manure to systems with solid manure.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.059
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0610.002

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.

Opus teacher head0.014
GPT teacher head0.244
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations95
Published2021
Admission routes1
Has abstractyes

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